diff --git a/lib/node_modules/@stdlib/math/base/special/README.md b/lib/node_modules/@stdlib/math/base/special/README.md
index 8c5fa1edf5e..a7f1a09a097 100644
--- a/lib/node_modules/@stdlib/math/base/special/README.md
+++ b/lib/node_modules/@stdlib/math/base/special/README.md
@@ -53,7 +53,7 @@ var fcns = special;
- [`expm1rel( x )`][@stdlib/math/base/special/expm1rel]: compute the relative error exponential.
- [`kernelLog1p( f )`][@stdlib/math/base/special/kernel-log1p]: compute `log(1+f) - f` for `1+f` in `~[sqrt(2)/2, sqrt(2)]`.
- [`ln( x )`][@stdlib/math/base/special/ln]: evaluate the natural logarithm of a double-precision floating-point number.
-- [`log( x, b )`][@stdlib/math/base/special/log]: compute the base `b` logarithm.
+- [`log( x, b )`][@stdlib/math/base/special/log]: compute the base `b` logarithm of a double-precision floating-point number.
- [`log10( x )`][@stdlib/math/base/special/log10]: evaluate the common logarithm.
- [`log1mexp( x )`][@stdlib/math/base/special/log1mexp]: evaluates the natural logarithm of `1-exp(-|x|)`.
- [`log1p( x )`][@stdlib/math/base/special/log1p]: evaluate the natural logarithm of `1+x`.
diff --git a/lib/node_modules/@stdlib/repl/help/data/data.csv b/lib/node_modules/@stdlib/repl/help/data/data.csv
index 7ff372638ab..4eb501dc990 100644
--- a/lib/node_modules/@stdlib/repl/help/data/data.csv
+++ b/lib/node_modules/@stdlib/repl/help/data/data.csv
@@ -1090,7 +1090,7 @@ base.ldexp,"\nbase.ldexp( frac, exp )\n Multiplies a double-precision floatin
base.leftPad,"\nbase.leftPad( str, len, pad )\n Left pads a string such that the padded string has a length of at least\n `len`.\n\n An output string is not guaranteed to have a length of exactly `len`, but to\n have a length of at least `len`. To generate a padded string having a length\n equal to `len`, post-process a padded string by trimming off excess\n characters.\n\n Parameters\n ----------\n str: string\n Input string.\n\n len: integer\n Minimum string length.\n\n pad: string\n String used to pad.\n\n Returns\n -------\n out: string\n Padded string.\n\n Examples\n --------\n > var out = base.leftPad( 'a', 5, ' ' )\n ' a'\n > out = base.leftPad( 'beep', 10, 'b' )\n 'bbbbbbbeep'\n > out = base.leftPad( 'boop', 12, 'beep' )\n 'beepbeepboop'\n\n See Also\n --------\n base.rightPad\n"
base.leftTrim,"\nbase.leftTrim( str )\n Trims whitespace from the beginning of a string.\n\n \"Whitespace\" is defined as the following characters:\n\n - \f\n - \n\n - \r\n - \t\n - \v\n - \u0020\n - \u00a0\n - \u1680\n - \u2000-\u200a\n - \u2028\n - \u2029\n - \u202f\n - \u205f\n - \u3000\n - \ufeff\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Trimmed string.\n\n Examples\n --------\n > var out = base.leftTrim( ' \r\n\t Beep \t\t\n ' )\n 'Beep \t\t\n '\n\n See Also\n --------\n base.rightTrim, base.trim\n"
base.ln,"\nbase.ln( x )\n Evaluates the natural logarithm of a double-precision floating-point number.\n\n For negative numbers, the natural logarithm is not defined.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.ln( 4.0 )\n ~1.386\n > y = base.ln( 0.0 )\n -Infinity\n > y = base.ln( PINF )\n Infinity\n > y = base.ln( NaN )\n NaN\n > y = base.ln( -4.0 )\n NaN\n\n See Also\n --------\n base.exp, base.log10, base.log1p, base.log2\n"
-base.log,"\nbase.log( x, b )\n Computes the base `b` logarithm of `x`.\n\n For negative `b` or `x`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n b: number\n Base.\n\n Returns\n -------\n y: number\n Logarithm (base `b`).\n\n Examples\n --------\n > var y = base.log( 100.0, 10.0 )\n 2.0\n > y = base.log( 16.0, 2.0 )\n 4.0\n > y = base.log( 5.0, 1.0 )\n Infinity\n > y = base.log( NaN, 2.0 )\n NaN\n > y = base.log( 1.0, NaN )\n NaN\n > y = base.log( -4.0, 2.0 )\n NaN\n > y = base.log( 4.0, -2.0 )\n NaN\n\n See Also\n --------\n base.exp, base.ln, base.log10, base.log1p, base.log2\n"
+base.log,"\nbase.log( x, b )\n Computes the base `b` logarithm of a double-precision floating-point number.\n\n For negative `b` or `x`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n b: number\n Base.\n\n Returns\n -------\n y: number\n Logarithm (base `b`).\n\n Examples\n --------\n > var y = base.log( 100.0, 10.0 )\n 2.0\n > y = base.log( 16.0, 2.0 )\n 4.0\n > y = base.log( 5.0, 1.0 )\n Infinity\n > y = base.log( NaN, 2.0 )\n NaN\n > y = base.log( 1.0, NaN )\n NaN\n > y = base.log( -4.0, 2.0 )\n NaN\n > y = base.log( 4.0, -2.0 )\n NaN\n\n See Also\n --------\n base.exp, base.ln, base.log10, base.log1p, base.log2\n"
base.log1mexp,"\nbase.log1mexp( x )\n Evaluates the natural logarithm of `1-exp(-|x|)`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.log1mexp( -10.0 )\n ~-0.00005\n > y = base.log1mexp( 0.0 )\n -Infinity\n > y = base.log1mexp( 5.0 )\n ~-0.00676\n > y = base.log1mexp( 10.0 )\n ~-0.00005\n > y = base.log1mexp( NaN )\n NaN\n\n See Also\n --------\n base.exp, base.ln, base.log1p, base.log1pexp"
base.log1p,"\nbase.log1p( x )\n Evaluates the natural logarithm of `1+x`.\n\n For `x < -1`, the function returns `NaN`, as the natural logarithm is not\n defined for negative numbers.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.log1p( 4.0 )\n ~1.609\n > y = base.log1p( -1.0 )\n -Infinity\n > y = base.log1p( 0.0 )\n 0.0\n > y = base.log1p( -0.0 )\n -0.0\n > y = base.log1p( -2.0 )\n NaN\n > y = base.log1p( NaN )\n NaN\n\n See Also\n --------\n base.ln, base.log\n"
base.log1pexp,"\nbase.log1pexp( x )\n Evaluates the natural logarithm of `1+exp(x)`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.log1pexp( -10.0 )\n ~0.000045\n > y = base.log1pexp( 0.0 )\n ~0.693147\n > y = base.log1pexp( 5.0 )\n ~5.006715\n > y = base.log1pexp( 34.0 )\n 34.0\n > y = base.log1pexp( NaN )\n NaN\n\n See Also\n --------\n base.exp, base.ln, base.log1mexp, base.log1p"
diff --git a/lib/node_modules/@stdlib/repl/help/data/data.json b/lib/node_modules/@stdlib/repl/help/data/data.json
index c9c8a679e1a..dd2009fbf36 100644
--- a/lib/node_modules/@stdlib/repl/help/data/data.json
+++ b/lib/node_modules/@stdlib/repl/help/data/data.json
@@ -1 +1 @@
-{"abs":"\nabs( x[, options] )\n Computes the absolute value.\n\n If provided a number, the function returns a number.\n\n If provided an ndarray or array-like object, the function performs element-\n wise computation.\n\n If provided an array-like object, the function returns an array-like object\n having the same length and data type as `x`.\n\n If provided an ndarray, the function returns an ndarray having the same\n shape and data type as `x`.\n\n Parameters\n ----------\n x: ndarray|ArrayLikeObject|number\n Input value.\n\n options: Object (optional)\n Options.\n\n options.order: string (optional)\n Output array order (either row-major (C-style) or column-major (Fortran-\n style)). Only applicable when the input array is an ndarray. By default,\n the output array order is inferred from the input array.\n\n options.dtype: string (optional)\n Output array data type. Only applicable when the input array is either\n an ndarray or array-like object. By default, the output array data type\n is inferred from the input array.\n\n Returns\n -------\n y: ndarray|ArrayLikeObject|number\n Results.\n\n Examples\n --------\n // Provide a number:\n > var y = abs( -1.0 )\n 1.0\n\n // Provide an array-like object:\n > var x = new Float64Array( [ -1.0, -2.0 ] );\n > y = abs( x )\n [ 1.0, 2.0 ]\n\n > x = [ -1.0, -2.0 ];\n > y = abs( x )\n [ 1.0, 2.0 ]\n\n // Provide an ndarray:\n > x = array( [ [ -1.0, -2.0 ], [ -3.0, -4.0 ] ] );\n > y = abs( x )\n \n > y.get( 0, 1 )\n 2.0\n\n\nabs.assign( x, y )\n Computes the absolute value and assigns results to a provided output array.\n\n Parameters\n ----------\n x: ndarray|ArrayLikeObject\n Input array.\n\n y: ndarray|ArrayLikeObject\n Output array. Must be the same data \"kind\" (i.e., ndarray or array-like\n object) as the input array.\n\n Returns\n -------\n y: ndarray|ArrayLikeObject\n Output array.\n\n Examples\n --------\n // Provide an array-like object:\n > var x = new Float64Array( [ -1.0, -2.0 ] );\n > var y = new Float64Array( x.length );\n > var out = abs.assign( x, y )\n [ 1.0, 2.0 ]\n > var bool = ( out === y )\n true\n\n > x = [ -1.0, -2.0 ];\n > y = [ 0.0, 0.0 ];\n > out = abs.assign( x, y )\n [ 1.0, 2.0 ]\n > bool = ( out === y )\n true\n\n // Provide an ndarray:\n > x = array( [ [ -1.0, -2.0 ], [ -3.0, -4.0 ] ] );\n > y = array( [ [ 0.0, 0.0 ], [ 0.0, 0.0 ] ] );\n > out = abs.assign( x, y )\n \n > out.get( 0, 1 )\n 2.0\n > bool = ( out === y )\n true\n\n","abs.assign":"\nabs.assign( x, y )\n Computes the absolute value and assigns results to a provided output array.\n\n Parameters\n ----------\n x: ndarray|ArrayLikeObject\n Input array.\n\n y: ndarray|ArrayLikeObject\n Output array. Must be the same data \"kind\" (i.e., ndarray or array-like\n object) as the input array.\n\n Returns\n -------\n y: ndarray|ArrayLikeObject\n Output array.\n\n Examples\n --------\n // Provide an array-like object:\n > var x = new Float64Array( [ -1.0, -2.0 ] );\n > var y = new Float64Array( x.length );\n > var out = abs.assign( x, y )\n [ 1.0, 2.0 ]\n > var bool = ( out === y )\n true\n\n > x = [ -1.0, -2.0 ];\n > y = [ 0.0, 0.0 ];\n > out = abs.assign( x, y )\n [ 1.0, 2.0 ]\n > bool = ( out === y )\n true\n\n // Provide an ndarray:\n > x = array( [ [ -1.0, -2.0 ], [ -3.0, -4.0 ] ] );\n > y = array( [ [ 0.0, 0.0 ], [ 0.0, 0.0 ] ] );\n > out = abs.assign( x, y )\n \n > out.get( 0, 1 )\n 2.0\n > bool = ( out === y )\n true","acartesianPower":"\nacartesianPower( x, n )\n Returns the Cartesian power.\n\n If provided an empty array, the function returns an empty array.\n\n If `n` is less than or equal to zero, the function returns an empty array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n n: integer\n Power.\n\n Returns\n -------\n out: Array\n Cartesian product.\n\n Examples\n --------\n > var x = [ 1, 2 ];\n > var out = acartesianPower( x, 2 )\n [ [ 1, 1 ], [ 1, 2 ], [ 2, 1 ], [ 2, 2 ] ]\n\n See Also\n --------\n acartesianProduct, acartesianSquare\n","acartesianProduct":"\nacartesianProduct( x1, x2 )\n Returns the Cartesian product.\n\n If provided one or more empty arrays, the function returns an empty array.\n\n Parameters\n ----------\n x1: ArrayLikeObject\n First input array.\n\n x2: ArrayLikeObject\n Second input array.\n\n Returns\n -------\n out: Array\n Cartesian product.\n\n Examples\n --------\n > var x1 = [ 1, 2 ];\n > var x2 = [ 3, 4 ];\n > var out = acartesianProduct( x1, x2 )\n [ [ 1, 3 ], [ 1, 4 ], [ 2, 3 ], [ 2, 4 ] ]\n\n See Also\n --------\n acartesianPower, acartesianSquare\n","acartesianSquare":"\nacartesianSquare( x )\n Returns the Cartesian square.\n\n If provided an empty array, the function returns an empty array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n Returns\n -------\n out: Array\n Cartesian product.\n\n Examples\n --------\n > var out = acartesianSquare( [ 1, 2 ] )\n [ [ 1, 1 ], [ 1, 2 ], [ 2, 1 ], [ 2, 2 ] ]\n\n See Also\n --------\n acartesianPower, acartesianProduct\n","acronym":"\nacronym( str[, options] )\n Generates an acronym for a given string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n options: Object (optional)\n Options.\n\n options.stopwords: Array (optional)\n Array of custom stop words.\n\n Returns\n -------\n out: string\n Acronym for the given string.\n\n Examples\n --------\n > var out = acronym( 'the quick brown fox' )\n 'QBF'\n > out = acronym( 'Hard-boiled eggs' )\n 'HBE'\n","aempty":"\naempty( length[, dtype] )\n Creates an uninitialized array having a specified length.\n\n In browser environments, the function always returns zero-filled arrays.\n\n If `dtype` is 'generic', the function always returns a zero-filled array.\n\n In Node.js versions >=3.0.0, the underlying memory of returned typed arrays\n is *not* initialized. Memory contents are unknown and may contain\n *sensitive* data.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = aempty( 2 )\n \n > arr = aempty( 2, 'float32' )\n \n\n See Also\n --------\n aemptyLike, afull, aones, azeros, ndempty\n","aemptyLike":"\naemptyLike( x[, dtype] )\n Creates an uninitialized array having the same length and data type as a\n provided input array.\n\n In browser environments, the function always returns zero-filled arrays.\n\n If `dtype` is 'generic', the function always returns a zero-filled array.\n\n In Node.js versions >=3.0.0, the underlying memory of returned typed arrays\n is *not* initialized. Memory contents are unknown and may contain\n *sensitive* data.\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var arr = aemptyLike( x )\n \n > arr = aemptyLike( x, 'float32' )\n \n\n See Also\n --------\n aempty, afullLike, aonesLike, azerosLike, ndemptyLike\n","AFINN_96":"\nAFINN_96()\n Returns a list of English words rated for valence.\n\n The returned list contains 1468 English words (and phrases) rated for\n valence. Negative words have a negative valence [-5,0). Positive words have\n a positive valence (0,5]. Neutral words have a valence of 0.\n\n A few notes:\n\n - The list is an earlier version of AFINN-111.\n - The list includes misspelled words. Their presence is intentional, as such\n misspellings frequently occur in social media content.\n - All words are lowercase.\n - Some \"words\" are phrases; e.g., \"cashing in\", \"cool stuff\".\n - Words may contain apostrophes; e.g., \"can't stand\".\n - Words may contain dashes; e.g., \"cover-up\", \"made-up\".\n\n Returns\n -------\n out: Array\n List of English words and their valence.\n\n Examples\n --------\n > var list = AFINN_96()\n [ [ 'abandon', -2 ], [ 'abandons', -2 ], [ 'abandoned', -2 ], ... ]\n\n References\n ----------\n - Nielsen, Finn Årup. 2011. \"A new ANEW: Evaluation of a word list for\n sentiment analysis in microblogs.\" In *Proceedings of the ESWC2011 Workshop\n on 'Making Sense of Microposts': Big Things Come in Small Packages.*,\n 718:93–98. CEUR Workshop Proceedings. .\n\n * If you use the list for publication or third party consumption, please\n cite the listed reference.\n\n See Also\n --------\n AFINN_111\n","AFINN_111":"\nAFINN_111()\n Returns a list of English words rated for valence.\n\n The returned list contains 2477 English words (and phrases) rated for\n valence. Negative words have a negative valence [-5,0). Positive words have\n a positive valence (0,5]. Neutral words have a valence of 0.\n\n A few notes:\n\n - The list includes misspelled words. Their presence is intentional, as such\n misspellings frequently occur in social media content.\n - All words are lowercase.\n - Words may contain numbers; e.g., \"n00b\".\n - Some \"words\" are phrases; e.g., \"cool stuff\", \"not good\".\n - Words may contain apostrophes; e.g., \"can't stand\".\n - Words may contain diaeresis; e.g., \"naïve\".\n - Words may contain dashes; e.g., \"self-deluded\", \"self-confident\".\n\n Returns\n -------\n out: Array\n List of English words and their valence.\n\n Examples\n --------\n > var list = AFINN_111()\n [ [ 'abandon', -2 ], [ 'abandoned', -2 ], [ 'abandons', -2 ], ... ]\n\n References\n ----------\n - Nielsen, Finn Årup. 2011. \"A new ANEW: Evaluation of a word list for\n sentiment analysis in microblogs.\" In *Proceedings of the ESWC2011 Workshop\n on 'Making Sense of Microposts': Big Things Come in Small Packages.*,\n 718:93–98. CEUR Workshop Proceedings. .\n\n * If you use the list for publication or third party consumption, please\n cite the listed reference.\n\n See Also\n --------\n AFINN_96\n","afull":"\nafull( length, value[, dtype] )\n Returns a filled array having a specified length.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n value: any\n Fill value.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = afull( 2, 1.0 )\n [ 1.0, 1.0 ]\n > arr = afull( 2, 1.0, 'float32' )\n [ 1.0, 1.0 ]\n\n See Also\n --------\n afullLike, aones, azeros\n","afullLike":"\nafullLike( x[, dtype] )\n Returns a filled array having the same length and data type as a provided\n input array.\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var y = afullLike( x, 1.0 )\n [ 1.0, 1.0 ]\n > y = afullLike( x, 1.0, 'float32' )\n [ 1.0, 1.0 ]\n\n See Also\n --------\n afull, aonesLike, azerosLike\n","alias2pkg":"\nalias2pkg( alias )\n Returns the package name associated with a provided alias.\n\n Parameters\n ----------\n alias: string\n Alias.\n\n Returns\n -------\n out: string|null\n Package name.\n\n Examples\n --------\n > var v = alias2pkg( 'base.sin' )\n '@stdlib/math/base/special/sin'\n\n See Also\n --------\n alias2related, aliases, pkg2alias\n","alias2related":"\nalias2related( alias )\n Returns aliases related to a specified alias.\n\n Parameters\n ----------\n alias: string\n Alias.\n\n Returns\n -------\n out: Array|null\n Related aliases.\n\n Examples\n --------\n > var v = alias2related( 'base.sin' )\n [...]\n\n See Also\n --------\n alias2pkg, aliases, pkg2related\n","alias2standalone":"\nalias2standalone( alias )\n Returns the standalone package name associated with a provided alias.\n\n Parameters\n ----------\n alias: string\n Alias.\n\n Returns\n -------\n out: string|null\n Standalone package name.\n\n Examples\n --------\n > var v = alias2standalone( 'base.sin' )\n '@stdlib/math-base-special-sin'\n\n See Also\n --------\n alias2pkg, alias2related, aliases, pkg2alias, pkg2standalone\n","aliases":"\naliases( [namespace] )\n Returns a list of standard library aliases.\n\n Parameters\n ----------\n namespace: string (optional)\n Namespace filter.\n\n Returns\n -------\n out: Array\n List of aliases.\n\n Examples\n --------\n > var o = aliases()\n [...]\n > o = aliases( '@stdlib/math/base/special' )\n [...]\n\n See Also\n --------\n alias2pkg, alias2related, pkg2alias\n","allocUnsafe":"\nallocUnsafe( size )\n Allocates a buffer having a specified number of bytes.\n\n The underlying memory of returned buffers is not initialized. Memory\n contents are unknown and may contain sensitive data.\n\n When the size is less than half a buffer pool size, memory is allocated from\n the buffer pool for faster allocation of Buffer instances.\n\n Parameters\n ----------\n size: integer\n Number of bytes to allocate.\n\n Returns\n -------\n out: Buffer\n Buffer instance.\n\n Examples\n --------\n > var buf = allocUnsafe( 100 )\n \n\n See Also\n --------\n Buffer, array2buffer, arraybuffer2buffer, copyBuffer, string2buffer\n","amskfilter":"\namskfilter( x, mask )\n Returns a new array by applying a mask to a provided input array.\n\n If a mask array element is truthy, the corresponding element in `x` is\n included in the output array; otherwise, the corresponding element in `x` is\n \"masked\" and thus excluded from the output array.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n mask: Array|TypedArray|Object\n Mask array.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var y = amskfilter( x, [ 0, 1, 0, 1 ] )\n [ 2, 4 ]\n\n See Also\n --------\n amskreject\n","amskput":"\namskput( x, mask, values[, options] )\n Replaces elements of an array with provided values according to a provided\n mask array.\n\n In broadcasting modes, the function supports broadcasting a values array\n containing a single element against the number of falsy values in the mask\n array.\n\n In repeat mode, the function supports recycling elements in a values array\n to satisfy the number of falsy values in the mask array.\n\n The function mutates the input array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n mask: ArrayLikeObject\n Mask array. If a mask array element is falsy, the corresponding element\n in `x` is *replaced*; otherwise, the corresponding element in `x` is\n \"masked\" and thus left unchanged.\n\n values: ArrayLikeObject\n Values to set.\n\n options: Object (optional)\n Function options.\n\n options.mode: string (optional)\n String specifying behavior when the number of values to set does not\n equal the number of falsy mask values. The function supports the\n following modes:\n\n - 'strict': specifies that the function must raise an exception when the\n number of values does not *exactly* equal the number of falsy mask\n values.\n - 'non_strict': specifies that the function must raise an exception when\n the function is provided insufficient values to satisfy the mask array.\n - 'strict_broadcast': specifies that the function must broadcast a\n single-element values array and otherwise raise an exception when the\n number of values does not **exactly** equal the number of falsy mask\n values.\n - 'broadcast': specifies that the function must broadcast a single-\n element values array and otherwise raise an exception when the function\n is provided insufficient values to satisfy the mask array.\n - 'repeat': specifies that the function must reuse provided values when\n replacing elements in `x` in order to satisfy the mask array.\n\n Default: 'repeat'.\n\n Returns\n -------\n out: ArrayLikeObject\n Input array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var out = amskput( x, [ 1, 0, 1, 0 ], [ 20, 40 ] )\n [ 1, 20, 3, 40 ]\n > var bool = ( out === x )\n true\n\n See Also\n --------\n aplace, aput, atake\n","amskreject":"\namskreject( x, mask )\n Returns a new array by applying a mask to a provided input array.\n\n If a mask array element is falsy, the corresponding element in `x` is\n included in the output array; otherwise, the corresponding element in `x` is\n \"masked\" and thus excluded from the output array.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n mask: Array|TypedArray|Object\n Mask array.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var y = amskreject( x, [ 0, 1, 0, 1 ] )\n [ 1, 3 ]\n\n See Also\n --------\n amskfilter\n","anans":"\nanans( length[, dtype] )\n Returns an array filled with NaNs and having a specified length.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - generic: generic JavaScript values\n\n The default array data type is `float64`.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = anans( 2 )\n [ NaN, NaN ]\n > arr = anans( 2, 'float32' )\n [ NaN, NaN ]\n\n See Also\n --------\n afull, anansLike, aones, azeros\n","anansLike":"\nanansLike( x[, dtype] )\n Returns an array filled with NaNs and having the same length and data type\n as a provided input array.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - generic: generic JavaScript values\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var y = anansLike( x )\n [ NaN, NaN ]\n > y = anansLike( x, 'float32' )\n [ NaN, NaN ]\n\n See Also\n --------\n afullLike, anans, aonesLike, azerosLike\n","anova1":"\nanova1( x, factor[, options] )\n Performs a one-way analysis of variance.\n\n Parameters\n ----------\n x: Array\n Measured values.\n\n factor: Array\n Array of treatments.\n\n options: Object (optional)\n Options.\n\n options.alpha: number (optional)\n Number in the interval `[0,1]` giving the significance level of the\n hypothesis test. Default: `0.05`.\n\n Returns\n -------\n out: Object\n Test result object.\n\n out.alpha: number\n Significance level.\n\n out.rejected: boolean\n Test decision.\n\n out.pValue: number\n p-value of the test.\n\n out.statistic: number\n Value of test statistic.\n\n out.method: string\n Name of test.\n\n out.means: Object\n Group means alongside sample sizes and standard errors.\n\n out.treatment: Object\n Treatment results.\n\n out.treatment.df: number\n Treatment degrees of freedom.\n\n out.treatment.ss: number\n Treatment sum of squares.\n\n out.treatment.ms: number\n Treatment mean sum of squares.\n\n out.error: Object\n Error results.\n\n out.error.df: number\n Error degrees of freedom.\n\n out.error.ss: number\n Error sum of squares.\n\n out.error.ms: number\n Error mean sum of squares.\n\n out.print: Function\n Function to print formatted output.\n\n Examples\n --------\n > var x = [1, 3, 5, 2, 4, 6, 8, 7, 10, 11, 12, 15];\n > var f = [\n ... 'control', 'treatA', 'treatB', 'treatC', 'control',\n ... 'treatA', 'treatB', 'treatC', 'control', 'treatA', 'treatB', 'treatC'\n ... ];\n > var out = anova1( x, f )\n {...}\n\n","ANSCOMBES_QUARTET":"\nANSCOMBES_QUARTET()\n Returns Anscombe's quartet.\n\n Anscombe's quartet is a set of 4 datasets which all have nearly identical\n simple statistical properties but vary considerably when graphed. Anscombe\n created the datasets to demonstrate why graphical data exploration should\n precede statistical data analysis and to show the effect of outliers on\n statistical properties.\n\n Returns\n -------\n out: Array\n Anscombe's quartet.\n\n Examples\n --------\n > var d = ANSCOMBES_QUARTET()\n [[[10,8.04],...],[[10,9.14],...],[[10,7.46],...],[[8,6.58],...]]\n\n References\n ----------\n - Anscombe, Francis J. 1973. \"Graphs in Statistical Analysis.\" *The American\n Statistician* 27 (1). [American Statistical Association, Taylor & Francis,\n Ltd.]: 17–21. .\n\n","any":"\nany( collection )\n Tests whether at least one element in a collection is truthy.\n\n The function immediately returns upon encountering a truthy value.\n\n If provided an empty collection, the function returns `false`.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n Returns\n -------\n bool: boolean\n The function returns `true` if an element is truthy; otherwise, the\n function returns `false`.\n\n Examples\n --------\n > var arr = [ 0, 0, 0, 0, 1 ];\n > var bool = any( arr )\n true\n\n See Also\n --------\n anyBy, every, forEach, none, some\n","anyBy":"\nanyBy( collection, predicate[, thisArg ] )\n Tests whether at least one element in a collection passes a test implemented\n by a predicate function.\n\n The predicate function is provided three arguments:\n\n - `value`: collection value\n - `index`: collection index\n - `collection`: the input collection\n\n The function immediately returns upon encountering a truthy return value.\n\n If provided an empty collection, the function returns `false`.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n predicate: Function\n The test function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n bool: boolean\n The function returns `true` if the predicate function returns `true` for\n any element; otherwise, the function returns `false`.\n\n Examples\n --------\n > function negative( v ) { return ( v < 0 ); };\n > var arr = [ 1, 2, 3, 4, -1 ];\n > var bool = anyBy( arr, negative )\n true\n\n See Also\n --------\n anyByAsync, anyByRight, everyBy, forEach, noneBy, someBy\n","anyByAsync":"\nanyByAsync( collection, [options,] predicate, done )\n Tests whether at least one element in a collection passes a test implemented\n by a predicate function.\n\n When invoked, the predicate function is provided a maximum of four\n arguments:\n\n - `value`: collection value\n - `index`: collection index\n - `collection`: the input collection\n - `next`: a callback to be invoked after processing a collection `value`\n\n The actual number of provided arguments depends on function length. If the\n predicate function accepts two arguments, the predicate function is\n provided:\n\n - `value`\n - `next`\n\n If the predicate function accepts three arguments, the predicate function is\n provided:\n\n - `value`\n - `index`\n - `next`\n\n For every other predicate function signature, the predicate function is\n provided all four arguments.\n\n The `next` callback takes two arguments:\n\n - `error`: error argument\n - `result`: test result\n\n If a provided function calls the `next` callback with a truthy `error`\n argument, the function suspends execution and immediately calls the `done`\n callback for subsequent `error` handling.\n\n The function immediately returns upon encountering a non-falsy `result`\n value and calls the `done` callback with `null` as the first argument and\n `true` as the second argument.\n\n If all elements fail, the function calls the `done` callback with `null`\n as the first argument and `false` as the second argument.\n\n Execution is *not* guaranteed to be asynchronous. To guarantee asynchrony,\n wrap the `done` callback in a function which either executes at the end of\n the current stack (e.g., `nextTick`) or during a subsequent turn of the\n event loop (e.g., `setImmediate`, `setTimeout`).\n\n The function does not support dynamic collection resizing.\n\n The function does not skip `undefined` elements.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n done: Function\n A callback invoked either upon processing all collection elements or\n upon encountering an error.\n\n Examples\n --------\n // Basic usage:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 3000, 2500, 1000 ];\n > anyByAsync( arr, predicate, done )\n 1000\n 2500\n 3000\n false\n\n // Limit number of concurrent invocations:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var opts = { 'limit': 2 };\n > var arr = [ 3000, 2500, 1000 ];\n > anyByAsync( arr, opts, predicate, done )\n 2500\n 3000\n 1000\n false\n\n // Process sequentially:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var opts = { 'series': true };\n > var arr = [ 3000, 2500, 1000 ];\n > anyByAsync( arr, opts, predicate, done )\n 3000\n 2500\n 1000\n false\n\n\nanyByAsync.factory( [options,] predicate )\n Returns a function which tests whether at least one element in a collection\n passes a test implemented by a predicate function.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n Returns\n -------\n out: Function\n A function which tests each element in a collection.\n\n Examples\n --------\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > var opts = { 'series': true };\n > var f = anyByAsync.factory( opts, predicate );\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 3000, 2500, 1000 ];\n > f( arr, done )\n 3000\n 2500\n 1000\n false\n > arr = [ 2000, 1500, 1000 ];\n > f( arr, done )\n 2000\n 1500\n 1000\n false\n\n See Also\n --------\n anyBy, anyByRightAsync, everyByAsync, forEachAsync, noneByAsync, someByAsync\n","anyByAsync.factory":"\nanyByAsync.factory( [options,] predicate )\n Returns a function which tests whether at least one element in a collection\n passes a test implemented by a predicate function.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n Returns\n -------\n out: Function\n A function which tests each element in a collection.\n\n Examples\n --------\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > var opts = { 'series': true };\n > var f = anyByAsync.factory( opts, predicate );\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 3000, 2500, 1000 ];\n > f( arr, done )\n 3000\n 2500\n 1000\n false\n > arr = [ 2000, 1500, 1000 ];\n > f( arr, done )\n 2000\n 1500\n 1000\n false\n\n See Also\n --------\n anyBy, anyByRightAsync, everyByAsync, forEachAsync, noneByAsync, someByAsync","anyByRight":"\nanyByRight( collection, predicate[, thisArg ] )\n Tests whether at least one element in a collection passes a test implemented\n by a predicate function, iterating from right to left.\n\n The predicate function is provided three arguments:\n\n - `value`: collection value\n - `index`: collection index\n - `collection`: the input collection\n\n The function immediately returns upon encountering a truthy return value.\n\n If provided an empty collection, the function returns `false`.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n predicate: Function\n The test function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n bool: boolean\n The function returns `true` if the predicate function returns `true` for\n any element; otherwise, the function returns `false`.\n\n Examples\n --------\n > function negative( v ) { return ( v < 0 ); };\n > var arr = [ -1, 1, 2, 3, 4 ];\n > var bool = anyByRight( arr, negative )\n true\n\n See Also\n --------\n anyBy, anyByRightAsync, everyByRight, forEachRight, noneByRight, someByRight\n","anyByRightAsync":"\nanyByRightAsync( collection, [options,] predicate, done )\n Tests whether at least one element in a collection passes a test implemented\n by a predicate function, iterating from right to left.\n\n When invoked, the predicate function is provided a maximum of four\n arguments:\n\n - `value`: collection value\n - `index`: collection index\n - `collection`: the input collection\n - `next`: a callback to be invoked after processing a collection `value`\n\n The actual number of provided arguments depends on function length. If the\n predicate function accepts two arguments, the predicate function is\n provided:\n\n - `value`\n - `next`\n\n If the predicate function accepts three arguments, the predicate function is\n provided:\n\n - `value`\n - `index`\n - `next`\n\n For every other predicate function signature, the predicate function is\n provided all four arguments.\n\n The `next` callback takes two arguments:\n\n - `error`: error argument\n - `result`: test result\n\n If a provided function calls the `next` callback with a truthy `error`\n argument, the function suspends execution and immediately calls the `done`\n callback for subsequent `error` handling.\n\n The function immediately returns upon encountering a non-falsy `result`\n value and calls the `done` callback with `null` as the first argument and\n `true` as the second argument.\n\n If all elements fail, the function calls the `done` callback with `null`\n as the first argument and `false` as the second argument.\n\n Execution is *not* guaranteed to be asynchronous. To guarantee asynchrony,\n wrap the `done` callback in a function which either executes at the end of\n the current stack (e.g., `nextTick`) or during a subsequent turn of the\n event loop (e.g., `setImmediate`, `setTimeout`).\n\n The function does not support dynamic collection resizing.\n\n The function does not skip `undefined` elements.\n\n Parameters\n ----------\n collection: Array|TypedArray|Object\n Input collection over which to iterate. If provided an object, the\n object must be array-like (excluding strings and functions).\n\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n done: Function\n A callback invoked either upon processing all collection elements or\n upon encountering an error.\n\n Examples\n --------\n // Basic usage:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 1000, 2500, 3000 ];\n > anyByRightAsync( arr, predicate, done )\n 1000\n 2500\n 3000\n false\n\n // Limit number of concurrent invocations:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var opts = { 'limit': 2 };\n > var arr = [ 1000, 2500, 3000 ];\n > anyByRightAsync( arr, opts, predicate, done )\n 2500\n 3000\n 1000\n false\n\n // Process sequentially:\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var opts = { 'series': true };\n > var arr = [ 1000, 2500, 3000 ];\n > anyByRightAsync( arr, opts, predicate, done )\n 3000\n 2500\n 1000\n false\n\n\nanyByRightAsync.factory( [options,] predicate )\n Returns a function which tests whether at least one element in a collection\n passes a test implemented by a predicate function, iterating from right to\n left.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n Returns\n -------\n out: Function\n A function which tests each element in a collection.\n\n Examples\n --------\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > var opts = { 'series': true };\n > var f = anyByRightAsync.factory( opts, predicate );\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 1000, 2500, 3000 ];\n > f( arr, done )\n 3000\n 2500\n 1000\n false\n > arr = [ 1000, 1500, 2000 ];\n > f( arr, done )\n 2000\n 1500\n 1000\n false\n\n See Also\n --------\n anyByAsync, anyByRight, everyByRightAsync, forEachRightAsync, noneByRightAsync, someByRightAsync\n","anyByRightAsync.factory":"\nanyByRightAsync.factory( [options,] predicate )\n Returns a function which tests whether at least one element in a collection\n passes a test implemented by a predicate function, iterating from right to\n left.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.limit: integer (optional)\n Maximum number of pending invocations. Default: Infinity.\n\n options.series: boolean (optional)\n Boolean indicating whether to process each collection element\n sequentially. Default: false.\n\n options.thisArg: any (optional)\n Execution context.\n\n predicate: Function\n The test function to invoke for each element in a collection.\n\n Returns\n -------\n out: Function\n A function which tests each element in a collection.\n\n Examples\n --------\n > function predicate( value, next ) {\n ... setTimeout( onTimeout, value );\n ... function onTimeout() {\n ... console.log( value );\n ... next( null, false );\n ... }\n ... };\n > var opts = { 'series': true };\n > var f = anyByRightAsync.factory( opts, predicate );\n > function done( error, bool ) {\n ... if ( error ) {\n ... throw error;\n ... }\n ... console.log( bool );\n ... };\n > var arr = [ 1000, 2500, 3000 ];\n > f( arr, done )\n 3000\n 2500\n 1000\n false\n > arr = [ 1000, 1500, 2000 ];\n > f( arr, done )\n 2000\n 1500\n 1000\n false\n\n See Also\n --------\n anyByAsync, anyByRight, everyByRightAsync, forEachRightAsync, noneByRightAsync, someByRightAsync","anyInBy":"anyInBy( object, predicate[, thisArg ] )\n Tests whether at least one value in an object passes a test implemented by\n a predicate function.\n\n The predicate function is provided three arguments:\n\n - `value`: the value of the current property being processed in the object\n - `key`: the key of the current property being processed in the object\n - `object`: the input object\n\n The function immediately returns upon encountering a truthy return value.\n\n If provided an empty object, the function returns `false`.\n\n Parameters\n ----------\n object: Object\n Input object over which to iterate. It must be non-null.\n\n predicate: Function\n The test function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n bool: boolean\n The function returns `true` if the predicate function returns `true` for\n any value; otherwise, it returns `false`.\n\n Examples\n --------\n > function isNegative(value) { return value < 0 }\n > var obj = { a: 1, b: -2, c: 3, d: 4 }\n > var result = anyInBy(obj, isNegative)\n true\n\n See Also\n --------\n anyBy, anyOwnBy, everyInBy, someInBy","anyOwnBy":"anyOwnBy( object, predicate[, thisArg ] )\n Tests whether at least one own property of an object passes a \n test implemented by a predicate function.\n\n The predicate function is provided three arguments:\n\n - `value`: property value\n - `index`: property key\n - `object`: the input object\n\n The function immediately returns upon encountering a truthy return\n value.\n\n If provided an empty object, the function returns `false`.\n\n Parameters\n ----------\n object: Object\n Input object.\n\n predicate: Function\n Test function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n bool: boolean\n The function returns `true` if the predicate function returns a truthy\n value for one own property; otherwise, the function returns `false`.\n\n Examples\n --------\n > function positive( v ) { return ( v > 0 ); };\n > var obj = { 'a': -1, 'b': 2, 'c': -3 };\n > var bool = anyOwnBy( obj, positive )\n true\n\n See Also\n --------\n anyBy, anyInBy, everyOwnBy, someOwnBy\n","aones":"\naones( length[, dtype] )\n Returns an array filled with ones and having a specified length.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - int32: 32-bit two's complement signed integers\n - uint32: 32-bit unsigned integers\n - int16: 16-bit two's complement signed integers\n - uint16: 16-bit unsigned integers\n - int8: 8-bit two's complement signed integers\n - uint8: 8-bit unsigned integers\n - uint8c: 8-bit unsigned integers clamped to 0-255\n - generic: generic JavaScript values\n\n The default array data type is `float64`.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = aones( 2 )\n [ 1.0, 1.0 ]\n > arr = aones( 2, 'float32' )\n [ 1.0, 1.0 ]\n\n See Also\n --------\n afull, anans, aonesLike, azeros\n","aonesLike":"\naonesLike( x[, dtype] )\n Returns an array filled with ones and having the same length and data type\n as a provided input array.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - int32: 32-bit two's complement signed integers\n - uint32: 32-bit unsigned integers\n - int16: 16-bit two's complement signed integers\n - uint16: 16-bit unsigned integers\n - int8: 8-bit two's complement signed integers\n - uint8: 8-bit unsigned integers\n - uint8c: 8-bit unsigned integers clamped to 0-255\n - generic: generic JavaScript values\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var y = aonesLike( x )\n [ 1.0, 1.0 ]\n > y = aonesLike( x, 'float32' )\n [ 1.0, 1.0 ]\n\n See Also\n --------\n afullLike, anansLike, aones, azerosLike\n","aoneTo":"\naoneTo( n[, dtype] )\n Generates a linearly spaced numeric array whose elements increment by 1\n starting from one.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - int32: 32-bit two's complement signed integers\n - uint32: 32-bit unsigned integers\n - int16: 16-bit two's complement signed integers\n - uint16: 16-bit unsigned integers\n - int8: 8-bit two's complement signed integers\n - uint8: 8-bit unsigned integers\n - uint8c: 8-bit unsigned integers clamped to 0-255\n - generic: generic JavaScript values\n\n The default array data type is `float64`.\n\n If `n` is equal to zero, the function returns an empty array.\n\n Parameters\n ----------\n n: integer\n Number of elements.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = aoneTo( 2 )\n [ 1.0, 2.0 ]\n > arr = aoneTo( 2, 'float32' )\n [ 1.0, 2.0 ]\n\n See Also\n --------\n afull, aones, aoneToLike, azeroTo\n","aoneToLike":"\naoneToLike( x[, dtype] )\n Generates a linearly spaced numeric array whose elements increment by 1\n starting from one and having the same length and data type as a provided\n input array.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - int32: 32-bit two's complement signed integers\n - uint32: 32-bit unsigned integers\n - int16: 16-bit two's complement signed integers\n - uint16: 16-bit unsigned integers\n - int8: 8-bit two's complement signed integers\n - uint8: 8-bit unsigned integers\n - uint8c: 8-bit unsigned integers clamped to 0-255\n - generic: generic JavaScript values\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = aoneToLike( [ 0, 0 ] )\n [ 1, 2 ]\n > arr = aoneToLike( [ 0, 0 ], 'float32' )\n [ 1.0, 2.0 ]\n\n See Also\n --------\n afullLike, aonesLike, aoneTo, azeroToLike\n","APERY":"\nAPERY\n Apéry's constant.\n\n Examples\n --------\n > APERY\n 1.2020569031595942\n\n","aplace":"\naplace( x, mask, values[, options] )\n Replaces elements of an array with provided values according to a provided\n mask array.\n\n In broadcasting modes, the function supports broadcasting a values array\n containing a single element against the number of truthy values in the mask\n array.\n\n In repeat mode, the function supports recycling elements in a values array\n to satisfy the number of truthy values in the mask array.\n\n The function mutates the input array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n mask: ArrayLikeObject\n Mask array. If a mask array element is truthy, the corresponding element\n in `x` is *replaced*; otherwise, the corresponding element in `x` is\n \"masked\" and thus left unchanged.\n\n values: ArrayLikeObject\n Values to set.\n\n options: Object (optional)\n Function options.\n\n options.mode: string (optional)\n String specifying behavior when the number of values to set does not\n equal the number of truthy mask values. The function supports the\n following modes:\n\n - 'strict': specifies that the function must raise an exception when the\n number of values does not *exactly* equal the number of truthy mask\n values.\n - 'non_strict': specifies that the function must raise an exception when\n the function is provided insufficient values to satisfy the mask array.\n - 'strict_broadcast': specifies that the function must broadcast a\n single-element values array and otherwise raise an exception when the\n number of values does not **exactly** equal the number of truthy mask\n values.\n - 'broadcast': specifies that the function must broadcast a single-\n element values array and otherwise raise an exception when the function\n is provided insufficient values to satisfy the mask array.\n - 'repeat': specifies that the function must reuse provided values when\n replacing elements in `x` in order to satisfy the mask array.\n\n Default: 'repeat'.\n\n Returns\n -------\n out: ArrayLikeObject\n Input array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var out = aplace( x, [ 0, 1, 0, 1 ], [ 20, 40 ] )\n [ 1, 20, 3, 40 ]\n > var bool = ( out === x )\n true\n\n See Also\n --------\n amskput, aput, atake\n","append":"\nappend( collection1, collection2 )\n Adds the elements of one collection to the end of another collection.\n\n If the input collection is a typed array, the output value does not equal\n the input reference and the underlying `ArrayBuffer` may *not* be the same\n as the `ArrayBuffer` belonging to the input view.\n\n For purposes of generality, always treat the output collection as distinct\n from the input collection.\n\n Parameters\n ----------\n collection1: Array|TypedArray|Object\n A collection. If the collection is an `Object`, the collection should be\n array-like.\n\n collection2: Array|TypedArray|Object\n A collection containing the elements to add. If the collection is an\n `Object`, the collection should be array-like.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Updated collection.\n\n Examples\n --------\n // Arrays:\n > var arr = [ 1.0, 2.0, 3.0, 4.0, 5.0 ];\n > arr = append( arr, [ 6.0, 7.0 ] )\n [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]\n\n // Typed arrays:\n > arr = new Float64Array( [ 1.0, 2.0 ] );\n > arr = append( arr, [ 3.0, 4.0 ] )\n [ 1.0, 2.0, 3.0, 4.0 ]\n\n // Array-like object:\n > arr = { 'length': 0 };\n > arr = append( arr, [ 1.0, 2.0 ] )\n { 'length': 2, '0': 1.0, '1': 2.0 }\n\n See Also\n --------\n prepend, push\n","aput":"\naput( x, indices, values[, options] )\n Replaces specified elements of an array with provided values.\n\n The function supports broadcasting a `values` array containing a single\n element against an `indices` array containing one or more elements.\n\n The function mutates the input array.\n\n Because each index is only validated at the time of replacing a particular\n element, mutation may occur even when one or more indices are out-of-bounds,\n including when the index mode indicates to raise an exception.\n\n If `indices` is an empty array, the function returns the input array\n unchanged.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n indices: ArrayLikeObject\n List of element indices.\n\n values: ArrayLikeObject\n Values to set. When `indices` contains one or more elements, `values`\n must be broadcast compatible with `indices` (i.e., must have either one\n element or the same number of elements as `indices`).\n\n options: Object (optional)\n Function options.\n\n options.mode: string (optional)\n Specifies how to handle an index outside the interval [0, max], where\n `max` is the maximum possible array index. If equal to 'throw', the\n function throws an error. If equal to 'normalize', the function throws\n an error if provided an out-of-bounds normalized index. If equal to\n 'wrap', the function wraps around an index using modulo arithmetic. If\n equal to 'clamp', the function sets an index to either 0 (minimum index)\n or the maximum index. Default: 'normalize'.\n\n Returns\n -------\n out: ArrayLikeObject\n Input array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var out = aput( x, [ 1, 3 ], [ 20, 40 ] )\n [ 1, 20, 3, 40 ]\n > var bool = ( out === x )\n true\n\n See Also\n --------\n amskput, aplace, atake\n","ARCH":"\nARCH\n Operating system CPU architecture for which the JavaScript runtime binary\n was compiled.\n\n Current possible values:\n\n - arm\n - arm64\n - ia32\n - mips\n - mipsel\n - ppc\n - ppc64\n - s390\n - s390x\n - x32\n - x64\n\n Examples\n --------\n > ARCH\n \n\n See Also\n --------\n PLATFORM\n","argumentFunction":"\nargumentFunction( idx )\n Returns a function which always returns a specified argument.\n\n The input argument corresponds to the zero-based index of the argument to\n return.\n\n Parameters\n ----------\n idx: integer\n Argument index to return (zero-based).\n\n Returns\n -------\n out: Function\n Argument function.\n\n Examples\n --------\n > var argn = argumentFunction( 1 );\n > var v = argn( 3.14, -3.14, 0.0 )\n -3.14\n > v = argn( -1.0, -0.0, 1.0 )\n -0.0\n > v = argn( 'beep', 'boop', 'bop' )\n 'boop'\n > v = argn( 'beep' )\n undefined\n\n See Also\n --------\n constantFunction, identity\n","ARGV":"\nARGV\n An array containing command-line arguments passed when launching the calling\n process.\n\n The first element is the absolute pathname of the executable that started\n the calling process.\n\n The second element is the path of the executed file.\n\n Any additional elements are additional command-line arguments.\n\n In browser environments, the array is empty.\n\n Examples\n --------\n > var execPath = ARGV[ 0 ]\n e.g., /usr/local/bin/node\n\n See Also\n --------\n ENV\n","array":"\narray( [buffer,] [options] )\n Returns a multidimensional array.\n\n Parameters\n ----------\n buffer: Array|TypedArray|Buffer|ndarray (optional)\n Data source.\n\n options: Object (optional)\n Options.\n\n options.buffer: Array|TypedArray|Buffer|ndarray (optional)\n Data source. If provided along with a `buffer` argument, the argument\n takes precedence.\n\n options.dtype: string (optional)\n Underlying storage data type. If not specified and a data source is\n provided, the data type is inferred from the provided data source. If an\n input data source is not of the same type, this option specifies the\n data type to which to cast the input data. For non-ndarray generic array\n data sources, the function casts generic array data elements to the\n default data type. In order to prevent this cast, the `dtype` option\n must be explicitly set to `'generic'`. Any time a cast is required, the\n `copy` option is set to `true`, as memory must be copied from the data\n source to an output data buffer. Default: 'float64'.\n\n options.order: string (optional)\n Specifies the memory layout of the data source as either row-major (C-\n style) or column-major (Fortran-style). The option may be one of the\n following values:\n\n - 'row-major': the order of the returned array is row-major.\n - 'column-major': the order of the returned array is column-major.\n - 'any': if a data source is column-major and not row-major, the order\n of the returned array is column-major; otherwise, the order of the\n returned array is row-major.\n - 'same': the order of the returned array matches the order of an input\n data source.\n\n Note that specifying an order which differs from the order of a\n provided data source does *not* entail a conversion from one memory\n layout to another. In short, this option is descriptive, not\n prescriptive. Default: 'row-major'.\n\n options.shape: Array (optional)\n Array shape (dimensions). If a shape is not specified, the function\n attempts to infer a shape based on a provided data source. For example,\n if provided a nested array, the function resolves nested array\n dimensions. If provided a multidimensional array data source, the\n function uses the array's associated shape. For most use cases, such\n inference suffices. For the remaining use cases, specifying a shape is\n necessary. For example, provide a shape to create a multidimensional\n array view over a linear data buffer, ignoring any existing shape meta\n data associated with a provided data source.\n\n options.flatten: boolean (optional)\n Boolean indicating whether to automatically flatten generic array data\n sources. If an array shape is not specified, the shape is inferred from\n the dimensions of nested arrays prior to flattening. If a use case\n requires partial flattening, partially flatten prior to invoking this\n function and set the option value to `false` to prevent further\n flattening during invocation. Default: true.\n\n options.copy: boolean (optional)\n Boolean indicating whether to (shallow) copy source data to a new data\n buffer. The function does *not* perform a deep copy. To prevent\n undesired shared changes in state for generic arrays containing objects,\n perform a deep copy prior to invoking this function. Default: false.\n\n options.ndmin: integer (optional)\n Specifies the minimum number of dimensions. If an array shape has fewer\n dimensions than required by `ndmin`, the function prepends singleton\n dimensions to the array shape in order to satisfy the dimensions\n requirement. Default: 0.\n\n options.casting: string (optional)\n Specifies the casting rule used to determine acceptable casts. The\n option may be one of the following values:\n\n - 'none': only allow casting between identical types.\n - 'equiv': allow casting between identical and byte swapped types.\n - 'safe': only allow \"safe\" casts.\n - 'mostly-safe': allow \"safe casts\" and, for floating-point data types,\n downcasts.\n - 'same-kind': allow \"safe\" casts and casts within the same kind (e.g.,\n between signed integers or between floats).\n - 'unsafe': allow casting between all types (including between integers\n and floats).\n\n Default: 'safe'.\n\n options.codegen: boolean (optional)\n Boolean indicating whether to use code generation. Code generation can\n boost performance, but may be problematic in browser contexts enforcing\n a strict content security policy (CSP). Default: true.\n\n options.mode: string (optional)\n Specifies how to handle indices which exceed array dimensions. The\n option may be one of the following values:\n\n - 'throw': an ndarray instance throws an error when an index exceeds\n array dimensions.\n - 'normalize': an ndarray instance normalizes negative indices and\n throws an error when an index exceeds array dimensions.\n - 'wrap': an ndarray instance wraps around indices exceeding array\n dimensions using modulo arithmetic.\n - 'clamp', an ndarray instance sets an index exceeding array dimensions\n to either `0` (minimum index) or the maximum index.\n\n Default: 'throw'.\n\n options.submode: Array (optional)\n Specifies how to handle subscripts which exceed array dimensions. If a\n mode for a corresponding dimension is equal to\n\n - 'throw': an ndarray instance throws an error when a subscript exceeds\n array dimensions.\n - 'normalize': an ndarray instance normalizes negative subscripts and\n throws an error when a subscript exceeds array dimensions.\n - 'wrap': an ndarray instance wraps around subscripts exceeding array\n dimensions using modulo arithmetic.\n - 'clamp': an ndarray instance sets a subscript exceeding array\n dimensions to either `0` (minimum index) or the maximum index.\n\n If the number of modes is fewer than the number of dimensions, the\n function recycles modes using modulo arithmetic.\n\n Default: [ options.mode ].\n\n options.readonly: boolean (optional)\n Boolean indicating whether an array should be read-only. Default: false.\n\n Returns\n -------\n out: ndarray\n Multidimensional array.\n\n Examples\n --------\n // Create a 2x2 matrix:\n > var arr = array( [ [ 1.0, 2.0 ], [ 3.0, 4.0 ] ] )\n \n\n // Get an element using subscripts:\n > var v = arr.get( 1, 1 )\n 4.0\n\n // Get an element using a linear index:\n > v = arr.iget( 3 )\n 4.0\n\n // Set an element using subscripts:\n > arr.set( 1, 1, 40.0 );\n > arr.get( 1, 1 )\n 40.0\n\n // Set an element using a linear index:\n > arr.iset( 3, 99.0 );\n > arr.get( 1, 1 )\n 99.0\n\n See Also\n --------\n ndarray\n","array2buffer":"\narray2buffer( arr )\n Allocates a buffer using an octet array.\n\n Parameters\n ----------\n arr: Array\n Array (or array-like object) of octets from which to copy.\n\n Returns\n -------\n out: Buffer\n Buffer instance.\n\n Examples\n --------\n > var buf = array2buffer( [ 1, 2, 3, 4 ] )\n [ 1, 2, 3, 4 ]\n\n See Also\n --------\n Buffer, arraybuffer2buffer, copyBuffer, string2buffer\n","array2fancy":"\narray2fancy( x[, options] )\n Converts an array to an object supporting fancy indexing.\n\n An array supporting fancy indexing is an array which supports slicing via\n indexing expressions for both retrieval and assignment.\n\n A fancy array shares the *same* data as the provided input array. Hence, any\n mutations to the returned array will affect the underlying input array and\n vice versa.\n\n For operations returning a new array (e.g., when slicing or invoking an\n instance method), a fancy array returns a new fancy array having the same\n configuration as specified by provided options.\n\n A fancy array supports indexing using positive and negative integers (both\n numeric literals and strings), Slice instances, subsequence expressions,\n mask arrays, boolean arrays, and integer arrays.\n\n A fancy array supports all properties and methods of the input array, and,\n thus, a fancy array can be consumed by any API which supports array-like\n objects.\n\n Indexing expressions provide a convenient and powerful means for creating\n and operating on array views; however, their use does entail a performance\n cost. Indexing expressions are best suited for interactive use (e.g., in the\n REPL) and scripting. For performance critical applications, prefer\n equivalent functional APIs supporting array-like objects.\n\n Fancy arrays support broadcasting in which assigned scalars and single-\n element arrays are repeated (without additional memory allocation) to match\n the length of a target array instance.\n\n Fancy array broadcasting follows the same rules as for ndarrays.\n\n Consequently, when assigning arrays to slices, the array on the right-hand-\n side must be broadcast-compatible with number of elements in the slice.\n\n Fancy arrays support (mostly) safe casts (i.e., any cast which can be\n performed without overflow or loss of precision, with the exception of\n floating-point arrays which are also allowed to downcast from higher\n precision to lower precision).\n\n When attempting to perform an unsafe cast, fancy arrays will raise an\n exception.\n\n When assigning a real-valued scalar to a complex number array (e.g.,\n Complex128Array or Complex64Array), a fancy array will cast the real-valued\n scalar to a complex number argument having an imaginary component equal to\n zero.\n\n In older JavaScript environments which do not support Proxy objects, the use\n of indexing expressions is not supported.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n options: Object (optional)\n Function options.\n\n options.strict: boolean (optional)\n Boolean indicating whether to enforce strict bounds checking. Default:\n false.\n\n options.cache: Object (optional)\n Cache for resolving array index objects. Must have a 'get' method which\n accepts a single argument: a string identifier associated with an array\n index. If an array index associated with a provided identifier exists,\n the 'get' method should return an object having the following\n properties:\n\n - data: the underlying index array.\n - type: the index type. Must be either 'mask', 'bool', or 'int'.\n - dtype: the data type of the underlying array.\n\n If an array index is not associated with a provided identifier, the\n 'get' method should return `null`.\n\n Default: `ArrayIndex`.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array supporting fancy indexing.\n\n Examples\n --------\n > var y = array2fancy( [ 1, 2, 3, 4 ] );\n > y[ '1::2' ]\n [ 2, 4 ]\n > y[ '::-1' ]\n [ 4, 3, 2, 1 ]\n\n\narray2fancy.factory( [options] )\n Returns a function for converting an array to an object supporting fancy\n indexing.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.strict: boolean (optional)\n Boolean indicating whether to enforce strict bounds checking by default.\n Default: false.\n\n options.cache: Object (optional)\n Cache for resolving array index objects. Must have a 'get' method which\n accepts a single argument: a string identifier associated with an array\n index. If an array index associated with a provided identifier exists,\n the 'get' method should return an object having the following\n properties:\n\n - data: the underlying index array.\n - type: the index type. Must be either 'mask', 'bool', or 'int'.\n - dtype: the data type of the underlying array.\n\n If an array index is not associated with a provided identifier, the\n 'get' method should return `null`.\n\n Default: `ArrayIndex`.\n\n Returns\n -------\n fcn: Function\n Function for converting an array to an object supporting fancy indexing.\n\n Examples\n --------\n > var f = array2fancy.factory();\n > var y = f( [ 1, 2, 3, 4 ] );\n > y[ '1::2' ]\n [ 2, 4 ]\n > y[ '::-1' ]\n [ 4, 3, 2, 1 ]\n\n\narray2fancy.idx( x[, options] )\n Wraps a provided array as an array index object.\n\n For documentation and usage, see `ArrayIndex`.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n options: Object (optional)\n Function options.\n\n options.persist: boolean (optional)\n Boolean indicating whether to continue persisting an index object after\n first usage. Default: false.\n\n Returns\n -------\n out: ArrayIndex\n ArrayIndex instance.\n\n Examples\n --------\n > var idx = array2fancy.idx( [ 1, 2, 3, 4 ] );\n\n See Also\n --------\n aslice, FancyArray\n","array2fancy.factory":"\narray2fancy.factory( [options] )\n Returns a function for converting an array to an object supporting fancy\n indexing.\n\n Parameters\n ----------\n options: Object (optional)\n Function options.\n\n options.strict: boolean (optional)\n Boolean indicating whether to enforce strict bounds checking by default.\n Default: false.\n\n options.cache: Object (optional)\n Cache for resolving array index objects. Must have a 'get' method which\n accepts a single argument: a string identifier associated with an array\n index. If an array index associated with a provided identifier exists,\n the 'get' method should return an object having the following\n properties:\n\n - data: the underlying index array.\n - type: the index type. Must be either 'mask', 'bool', or 'int'.\n - dtype: the data type of the underlying array.\n\n If an array index is not associated with a provided identifier, the\n 'get' method should return `null`.\n\n Default: `ArrayIndex`.\n\n Returns\n -------\n fcn: Function\n Function for converting an array to an object supporting fancy indexing.\n\n Examples\n --------\n > var f = array2fancy.factory();\n > var y = f( [ 1, 2, 3, 4 ] );\n > y[ '1::2' ]\n [ 2, 4 ]\n > y[ '::-1' ]\n [ 4, 3, 2, 1 ]","array2fancy.idx":"\narray2fancy.idx( x[, options] )\n Wraps a provided array as an array index object.\n\n For documentation and usage, see `ArrayIndex`.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n options: Object (optional)\n Function options.\n\n options.persist: boolean (optional)\n Boolean indicating whether to continue persisting an index object after\n first usage. Default: false.\n\n Returns\n -------\n out: ArrayIndex\n ArrayIndex instance.\n\n Examples\n --------\n > var idx = array2fancy.idx( [ 1, 2, 3, 4 ] );\n\n See Also\n --------\n aslice, FancyArray","array2iterator":"\narray2iterator( src[, mapFcn[, thisArg]] )\n Returns an iterator which iterates over the elements of an array-like\n object.\n\n When invoked, an input function is provided three arguments:\n\n - value: iterated value\n - index: iterated value index\n - src: source array-like object\n\n If an environment supports Symbol.iterator, the returned iterator is\n iterable.\n\n If an environment supports Symbol.iterator, the function explicitly does not\n invoke an array's `@@iterator` method, regardless of whether this method is\n defined. To convert an array to an implementation defined iterator, invoke\n this method directly.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Array-like object from which to create the iterator.\n\n mapFcn: Function (optional)\n Function to invoke for each iterated value.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n iterator: Object\n Iterator.\n\n iterator.next(): Function\n Returns an iterator protocol-compliant object containing the next\n iterated value (if one exists) and a boolean flag indicating whether the\n iterator is finished.\n\n iterator.return( [value] ): Function\n Finishes an iterator and returns a provided value.\n\n Examples\n --------\n > var it = array2iterator( [ 1, 2, 3, 4 ] );\n > var v = it.next().value\n 1\n > v = it.next().value\n 2\n\n See Also\n --------\n iterator2array, circarray2iterator, array2iteratorRight, stridedarray2iterator\n","array2iteratorRight":"\narray2iteratorRight( src[, mapFcn[, thisArg]] )\n Returns an iterator which iterates from right to left over the elements of\n an array-like object.\n\n When invoked, an input function is provided three arguments:\n\n - value: iterated value\n - index: iterated value index\n - src: source array-like object\n\n If an environment supports Symbol.iterator, the returned iterator is\n iterable.\n\n If an environment supports Symbol.iterator, the function explicitly does not\n invoke an array's `@@iterator` method, regardless of whether this method is\n defined. To convert an array to an implementation defined iterator, invoke\n this method directly.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Array-like object from which to create the iterator.\n\n mapFcn: Function (optional)\n Function to invoke for each iterated value.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n iterator: Object\n Iterator.\n\n iterator.next(): Function\n Returns an iterator protocol-compliant object containing the next\n iterated value (if one exists) and a boolean flag indicating whether the\n iterator is finished.\n\n iterator.return( [value] ): Function\n Finishes an iterator and returns a provided value.\n\n Examples\n --------\n > var it = array2iteratorRight( [ 1, 2, 3, 4 ] );\n > var v = it.next().value\n 4\n > v = it.next().value\n 3\n\n See Also\n --------\n iterator2array, array2iterator\n","ArrayBuffer":"\nArrayBuffer( size )\n Returns an array buffer having a specified number of bytes.\n\n Buffer contents are initialized to 0.\n\n Parameters\n ----------\n size: integer\n Number of bytes.\n\n Returns\n -------\n out: ArrayBuffer\n An array buffer.\n\n Examples\n --------\n > var buf = new ArrayBuffer( 5 )\n \n\n\nArrayBuffer.length\n Number of input arguments the constructor accepts.\n\n Examples\n --------\n > ArrayBuffer.length\n 1\n\n\nArrayBuffer.isView( arr )\n Returns a boolean indicating if provided an array buffer view.\n\n Parameters\n ----------\n arr: any\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating if an input argument is a buffer view.\n\n Examples\n --------\n > var arr = new Float64Array( 10 );\n > ArrayBuffer.isView( arr )\n true\n\n\nArrayBuffer.prototype.byteLength\n Read-only property which returns the length (in bytes) of the array buffer.\n\n Examples\n --------\n > var buf = new ArrayBuffer( 5 );\n > buf.byteLength\n 5\n\n\nArrayBuffer.prototype.slice( [start[, end]] )\n Copies the bytes of an array buffer to a new array buffer.\n\n Parameters\n ----------\n start: integer (optional)\n Index at which to start copying buffer contents (inclusive). If\n negative, the index is relative to the end of the buffer.\n\n end: integer (optional)\n Index at which to stop copying buffer contents (exclusive). If negative,\n the index is relative to the end of the buffer.\n\n Returns\n -------\n out: ArrayBuffer\n A new array buffer whose contents have been copied from the calling\n array buffer.\n\n Examples\n --------\n > var b1 = new ArrayBuffer( 10 );\n > var b2 = b1.slice( 2, 6 );\n > var bool = ( b1 === b2 )\n false\n > b2.byteLength\n 4\n\n See Also\n --------\n Buffer, Float32Array, Float64Array, Int16Array, Int32Array, Int8Array, SharedArrayBuffer, Uint16Array, Uint32Array, Uint8Array, Uint8ClampedArray\n","ArrayBuffer.length":"\nArrayBuffer.length\n Number of input arguments the constructor accepts.\n\n Examples\n --------\n > ArrayBuffer.length\n 1","ArrayBuffer.isView":"\nArrayBuffer.isView( arr )\n Returns a boolean indicating if provided an array buffer view.\n\n Parameters\n ----------\n arr: any\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating if an input argument is a buffer view.\n\n Examples\n --------\n > var arr = new Float64Array( 10 );\n > ArrayBuffer.isView( arr )\n true","ArrayBuffer.prototype.byteLength":"\nArrayBuffer.prototype.byteLength\n Read-only property which returns the length (in bytes) of the array buffer.\n\n Examples\n --------\n > var buf = new ArrayBuffer( 5 );\n > buf.byteLength\n 5","ArrayBuffer.prototype.slice":"\nArrayBuffer.prototype.slice( [start[, end]] )\n Copies the bytes of an array buffer to a new array buffer.\n\n Parameters\n ----------\n start: integer (optional)\n Index at which to start copying buffer contents (inclusive). If\n negative, the index is relative to the end of the buffer.\n\n end: integer (optional)\n Index at which to stop copying buffer contents (exclusive). If negative,\n the index is relative to the end of the buffer.\n\n Returns\n -------\n out: ArrayBuffer\n A new array buffer whose contents have been copied from the calling\n array buffer.\n\n Examples\n --------\n > var b1 = new ArrayBuffer( 10 );\n > var b2 = b1.slice( 2, 6 );\n > var bool = ( b1 === b2 )\n false\n > b2.byteLength\n 4\n\n See Also\n --------\n Buffer, Float32Array, Float64Array, Int16Array, Int32Array, Int8Array, SharedArrayBuffer, Uint16Array, Uint32Array, Uint8Array, Uint8ClampedArray","arraybuffer2buffer":"\narraybuffer2buffer( buf[, byteOffset[, length]] )\n Allocates a buffer from an ArrayBuffer.\n\n The behavior of this function varies across Node.js versions due to changes\n in the underlying Node.js APIs:\n\n - <3.0.0: the function copies ArrayBuffer bytes to a new Buffer instance.\n - >=3.0.0 and <5.10.0: if provided a byte offset, the function copies\n ArrayBuffer bytes to a new Buffer instance; otherwise, the function\n returns a view of an ArrayBuffer without copying the underlying memory.\n - <6.0.0: if provided an empty ArrayBuffer, the function returns an empty\n Buffer which is not an ArrayBuffer view.\n - >=6.0.0: the function returns a view of an ArrayBuffer without copying\n the underlying memory.\n\n Parameters\n ----------\n buf: ArrayBuffer\n Input array buffer.\n\n byteOffset: integer (optional)\n Index offset specifying the location of the first byte.\n\n length: integer (optional)\n Number of bytes to expose from the underlying ArrayBuffer.\n\n Returns\n -------\n out: Buffer\n Buffer instance.\n\n Examples\n --------\n > var ab = new ArrayBuffer( 10 )\n \n > var buf = arraybuffer2buffer( ab )\n \n > var len = buf.length\n 10\n > buf = arraybuffer2buffer( ab, 2, 6 )\n \n > len = buf.length\n 6\n\n See Also\n --------\n Buffer, array2buffer, copyBuffer, string2buffer\n","arrayCtors":"\narrayCtors( dtype )\n Returns an array constructor.\n\n The function returns constructors for the following data types:\n\n - float32: single-precision floating-point numbers.\n - float64: double-precision floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - complex128: double-precision complex floating-point numbers.\n - bool: boolean values.\n - generic: values of any type.\n - int16: signed 16-bit integers.\n - int32: signed 32-bit integers.\n - int8: signed 8-bit integers.\n - uint16: unsigned 16-bit integers.\n - uint32: unsigned 32-bit integers.\n - uint8: unsigned 8-bit integers.\n - uint8c: unsigned clamped 8-bit integers.\n\n Parameters\n ----------\n dtype: string\n Data type.\n\n Returns\n -------\n out: Function|null\n Constructor.\n\n Examples\n --------\n > var ctor = arrayCtors( 'float64' )\n \n > ctor = arrayCtors( 'float' )\n null\n\n See Also\n --------\n typedarrayCtors\n","arrayDataType":"\narrayDataType( array )\n Returns the data type of an array.\n\n If provided an argument having an unknown or unsupported type, the function\n returns `null`.\n\n Parameters\n ----------\n array: any\n Input value.\n\n Returns\n -------\n out: string|null\n Data type.\n\n Examples\n --------\n > var arr = new Float64Array( 10 );\n > var dt = arrayDataType( arr )\n 'float64'\n > dt = arrayDataType( 'beep' )\n null\n\n See Also\n --------\n arrayDataTypes\n","arrayDataTypes":"\narrayDataTypes( [kind] )\n Returns a list of array data types.\n\n When not provided a data type \"kind\", the function returns an array\n containing the following data types:\n\n - float32: single-precision floating-point numbers.\n - float64: double-precision floating-point numbers.\n - complex64: single-precision complex floating-point numbers.\n - complex128: double-precision complex floating-point numbers.\n - bool: boolean values.\n - generic: values of any type.\n - int16: signed 16-bit integers.\n - int32: signed 32-bit integers.\n - int8: signed 8-bit integers.\n - uint16: unsigned 16-bit integers.\n - uint32: unsigned 32-bit integers.\n - uint8: unsigned 8-bit integers.\n - uint8c: unsigned clamped 8-bit integers.\n\n The function supports the following data type \"kinds\":\n\n - floating_point: floating-point data types.\n - real_floating_point: real-valued floating-point data types.\n - complex_floating_point: complex-valued floating-point data types.\n - boolean: boolean data types.\n - integer: integer data types.\n - signed_integer: signed integer data types.\n - unsigned_integer: unsigned integer data types.\n - real: real-valued data types.\n - numeric: numeric data types.\n - typed: \"typed\" data types.\n - all: all data types.\n\n Additionally, the function supports extending the \"kinds\" listed above by\n appending a '_and_generic' suffix to the kind name (e.g., real_and_generic).\n\n Parameters\n ----------\n kind: string (optional)\n Data type kind.\n\n Returns\n -------\n out: Array\n List of array data types.\n\n Examples\n --------\n > var out = arrayDataTypes()\n [...]\n > out = arrayDataTypes( 'floating_point' )\n [...]\n > out = arrayDataTypes( 'floating_point_and_generic' )\n [...]\n\n See Also\n --------\n typedarrayDataTypes, ndarrayDataTypes\n","ArrayIndex":"\nArrayIndex( x[, options] )\n Wraps a provided array as an array index object.\n\n Array index instances have no explicit functionality; however, they are used\n by \"fancy\" arrays for element retrieval and assignment.\n\n By default, an instance is invalidated and removed from an internal cache\n immediately after a consumer resolves the underlying data associated with an\n instance using the `get` static method. Immediate invalidation and cache\n removal ensures that references to the underlying array are not the source\n of memory leaks.\n\n Because instances leverage an internal cache implementing the Singleton\n pattern, one must be sure to use the same constructor as consumers. If one\n uses a different constructor, the consumer will *not* be able to resolve the\n original wrapped array, as the consumer will attempt to resolve an instance\n in the wrong internal cache.\n\n Because non-persisted instances are freed after first use, in order to avoid\n holding onto memory and to allow garbage collection, one should avoid\n scenarios in which an instance is never used.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n options: Object (optional)\n Function options.\n\n options.persist: boolean (optional)\n Boolean indicating whether to continue persisting an index object after\n first usage. Default: false.\n\n Returns\n -------\n out: ArrayIndex\n ArrayIndex instance.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n\n\nArrayIndex.free( id )\n Frees the instance associated with a provided identifier.\n\n Parameters\n ----------\n id: string\n Instance identifier.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether an instance was successfully freed.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > // ...\n > ArrayIndex.free( idx.id )\n\n\nArrayIndex.get( id )\n Returns the array associated with the instance having a provided identifier.\n\n Parameters\n ----------\n id: string\n Instance identifier.\n\n Returns\n -------\n out: Object\n Object containing array data.\n\n out.data: Array|TypedArray|Object\n The underlying array associated with the provided identifier.\n\n out.type: string\n The type of array index.\n\n out.dtype: string\n The data type of the underlying array.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > ArrayIndex.get( idx.id )\n {...}\n\n\nArrayIndex.prototype.data\n Read-only property returning the underlying index array.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Array data type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.data\n [ 1, 2, 3, 4 ]\n\n\nArrayIndex.prototype.dtype\n Read-only property returning the underlying data type of the index array.\n\n Returns\n -------\n out: string\n Array data type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.dtype\n 'generic'\n\n\nArrayIndex.prototype.id\n Read-only property returning the unique identifier associated with an\n instance.\n\n Returns\n -------\n out: string\n String identifier.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.id\n \n\n\nArrayIndex.prototype.isCached\n Read-only property returning a boolean indicating whether an array index is\n actively cached.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether an array index is actively cached.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.isCached\n true\n\n\nArrayIndex.prototype.type\n Read-only property returning the array index type.\n\n Returns\n -------\n out: string\n Array index type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.type\n \n\n\nArrayIndex.prototype.toString()\n Serializes an instance as a string.\n\n Returns\n -------\n str: string\n Serialized string.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.toString()\n\n\nArrayIndex.prototype.toJSON()\n Serializes an instance as a JSON object.\n\n Returns\n -------\n obj: Object\n JSON object.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.toJSON()\n { 'type': 'ArrayIndex', 'data': [ 1, 2, 3, 4 ] }\n\n See Also\n --------\n array2fancy\n","ArrayIndex.free":"\nArrayIndex.free( id )\n Frees the instance associated with a provided identifier.\n\n Parameters\n ----------\n id: string\n Instance identifier.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether an instance was successfully freed.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > // ...\n > ArrayIndex.free( idx.id )","ArrayIndex.get":"\nArrayIndex.get( id )\n Returns the array associated with the instance having a provided identifier.\n\n Parameters\n ----------\n id: string\n Instance identifier.\n\n Returns\n -------\n out: Object\n Object containing array data.\n\n out.data: Array|TypedArray|Object\n The underlying array associated with the provided identifier.\n\n out.type: string\n The type of array index.\n\n out.dtype: string\n The data type of the underlying array.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > ArrayIndex.get( idx.id )\n {...}","ArrayIndex.prototype.data":"\nArrayIndex.prototype.data\n Read-only property returning the underlying index array.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Array data type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.data\n [ 1, 2, 3, 4 ]","ArrayIndex.prototype.dtype":"\nArrayIndex.prototype.dtype\n Read-only property returning the underlying data type of the index array.\n\n Returns\n -------\n out: string\n Array data type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.dtype\n 'generic'","ArrayIndex.prototype.id":"\nArrayIndex.prototype.id\n Read-only property returning the unique identifier associated with an\n instance.\n\n Returns\n -------\n out: string\n String identifier.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.id\n ","ArrayIndex.prototype.isCached":"\nArrayIndex.prototype.isCached\n Read-only property returning a boolean indicating whether an array index is\n actively cached.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether an array index is actively cached.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.isCached\n true","ArrayIndex.prototype.type":"\nArrayIndex.prototype.type\n Read-only property returning the array index type.\n\n Returns\n -------\n out: string\n Array index type.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.type\n ","ArrayIndex.prototype.toString":"\nArrayIndex.prototype.toString()\n Serializes an instance as a string.\n\n Returns\n -------\n str: string\n Serialized string.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.toString()","ArrayIndex.prototype.toJSON":"\nArrayIndex.prototype.toJSON()\n Serializes an instance as a JSON object.\n\n Returns\n -------\n obj: Object\n JSON object.\n\n Examples\n --------\n > var idx = new ArrayIndex( [ 1, 2, 3, 4 ] );\n > idx.toJSON()\n { 'type': 'ArrayIndex', 'data': [ 1, 2, 3, 4 ] }\n\n See Also\n --------\n array2fancy","arrayMinDataType":"\narrayMinDataType( value )\n Returns the minimum array data type of the closest \"kind\" necessary for\n storing a provided scalar value.\n\n The function does *not* provide precision guarantees for non-integer-valued\n numbers. In other words, the function returns the smallest possible\n floating-point (i.e., inexact) data type for storing numbers having\n decimals.\n\n Parameters\n ----------\n value: any\n Scalar value.\n\n Returns\n -------\n dt: string\n Array data type.\n\n Examples\n --------\n > var dt = arrayMinDataType( 3.141592653589793 )\n 'float32'\n > dt = arrayMinDataType( 3 )\n 'uint8'\n > dt = arrayMinDataType( -3 )\n 'int8'\n > dt = arrayMinDataType( '-3' )\n 'generic'\n\n See Also\n --------\n arrayDataTypes, arrayPromotionRules, arraySafeCasts\n","arrayMostlySafeCasts":"\narrayMostlySafeCasts( [dtype] )\n Returns a list of array data types to which a provided array data type can\n be safely cast and, for floating-point data types, can be downcast.\n\n If not provided an array data type, the function returns a casting table.\n\n If provided an unrecognized array data type, the function returns `null`.\n\n Parameters\n ----------\n dtype: any (optional)\n Array data type value.\n\n Returns\n -------\n out: Object|Array|null\n Array data types to which a data type can be cast.\n\n Examples\n --------\n > var out = arrayMostlySafeCasts( 'float32' )\n \n\n See Also\n --------\n convertArray, convertArraySame, arrayDataTypes, arraySafeCasts, arraySameKindCasts, ndarrayMostlySafeCasts\n","arrayNextDataType":"\narrayNextDataType( [dtype] )\n Returns the next larger array data type of the same kind.\n\n If not provided a data type, the function returns a table.\n\n If a data type does not have a next larger data type or the next larger type\n is not supported, the function returns `-1`.\n\n If provided an unrecognized data type, the function returns `null`.\n\n Parameters\n ----------\n dtype: string (optional)\n Array data type.\n\n Returns\n -------\n out: Object|string|integer|null\n Next larger type(s).\n\n Examples\n --------\n > var out = arrayNextDataType( 'float32' )\n 'float64'\n\n See Also\n --------\n arrayDataType, arrayDataTypes\n","arrayPromotionRules":"\narrayPromotionRules( [dtype1, dtype2] )\n Returns the array data type with the smallest size and closest \"kind\" to\n which array data types can be safely cast.\n\n If not provided data types, the function returns a type promotion table.\n\n If a data type to which data types can be safely cast does *not* exist (or\n is not supported), the function returns `-1`.\n\n If provided an unrecognized data type, the function returns `null`.\n\n Parameters\n ----------\n dtype1: any (optional)\n Array data type.\n\n dtype2: any (optional)\n Array data type.\n\n Returns\n -------\n out: Object|string|integer|null\n Promotion rule(s).\n\n Examples\n --------\n > var out = arrayPromotionRules( 'float32', 'int32' )\n 'float64'\n\n See Also\n --------\n arrayDataTypes, arraySafeCasts, ndarrayPromotionRules\n","arraySafeCasts":"\narraySafeCasts( [dtype] )\n Returns a list of array data types to which a provided array data type can\n be safely cast.\n\n If not provided an array data type, the function returns a casting table.\n\n If provided an unrecognized array data type, the function returns `null`.\n\n Parameters\n ----------\n dtype: any (optional)\n Array data type.\n\n Returns\n -------\n out: Object|Array|null\n Array data types to which a data type can be safely cast.\n\n Examples\n --------\n > var out = arraySafeCasts( 'float32' )\n \n\n See Also\n --------\n convertArray, convertArraySame, arrayDataTypes, arrayMostlySafeCasts, arraySameKindCasts, ndarraySafeCasts\n","arraySameKindCasts":"\narraySameKindCasts( [dtype] )\n Returns a list of array data types to which a provided array data type can\n be safely cast or cast within the same \"kind\".\n\n If not provided an array data type, the function returns a casting table.\n\n If provided an unrecognized array data type, the function returns `null`.\n\n Parameters\n ----------\n dtype: any (optional)\n Array data type.\n\n Returns\n -------\n out: Object|Array|null\n Array data types to which a data type can be safely cast or cast within\n the same \"kind\".\n\n Examples\n --------\n > var out = arraySameKindCasts( 'float32' )\n \n\n See Also\n --------\n convertArray, convertArraySame, arrayDataTypes, arraySafeCasts, ndarraySameKindCasts\n","arrayShape":"\narrayShape( arr )\n Determines array dimensions.\n\n Parameters\n ----------\n arr: ArrayLikeObject\n Input array.\n\n Returns\n -------\n out: Array\n Array shape.\n\n Examples\n --------\n > var out = arrayShape( [ [ 1, 2, 3 ], [ 4, 5, 6 ] ] )\n [ 2, 3 ]\n\n See Also\n --------\n ndarray\n","arrayStream":"\narrayStream( src[, options] )\n Creates a readable stream from an array-like object.\n\n In object mode, `null` is a reserved value. If an array contains `null`\n values (e.g., as a means to encode missing values), the stream will\n prematurely end. Consider an alternative encoding or filter `null` values\n prior to invocation.\n\n In binary mode, if an array contains `undefined` values, the stream will\n emit an error. Consider providing a custom serialization function or\n filtering `undefined` values prior to invocation.\n\n If a serialization function fails to return a string or Buffer, the stream\n emits an error.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Source value.\n\n options: Object (optional)\n Options.\n\n options.objectMode: boolean (optional)\n Specifies whether a stream should operate in \"objectMode\". Default:\n false.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of bytes to store in an internal buffer\n before pausing the stream.\n\n options.sep: string (optional)\n Separator used to join streamed data. This option is only applicable\n when a stream is not in \"objectMode\". Default: '\\n'.\n\n options.serialize: Function (optional)\n Serialization function. The default behavior is to serialize streamed\n values as JSON strings. This option is only applicable when a stream is\n not in \"objectMode\".\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n stream: ReadableStream\n Readable stream.\n\n Examples\n --------\n > function fcn( chunk ) { console.log( chunk.toString() ); };\n > var s = arrayStream( [ 1, 2, 3 ] );\n > var o = inspectSinkStream( fcn );\n > s.pipe( o );\n\n\narrayStream.factory( [options] )\n Returns a function for creating readable streams from array-like objects.\n\n Parameters\n ----------\n options: Object (optional)\n Options.\n\n options.objectMode: boolean (optional)\n Specifies whether a stream should operate in \"objectMode\". Default:\n false.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of bytes to store in an internal buffer\n before pausing streaming.\n\n options.sep: string (optional)\n Separator used to join streamed data. This option is only applicable\n when a stream is not in \"objectMode\". Default: '\\n'.\n\n options.serialize: Function (optional)\n Serialization function. The default behavior is to serialize streamed\n values as JSON strings. This option is only applicable when a stream is\n not in \"objectMode\".\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n fcn: Function\n Function for creating readable streams.\n\n Examples\n --------\n > var opts = { 'objectMode': true, 'highWaterMark': 64 };\n > var createStream = arrayStream.factory( opts );\n\n\narrayStream.objectMode( src[, options] )\n Returns an \"objectMode\" readable stream from an array-like object.\n\n In object mode, `null` is a reserved value. If an array contains `null`\n values (e.g., as a means to encode missing values), the stream will\n prematurely end. Consider an alternative encoding or filter `null` values\n prior to invocation.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Source value.\n\n options: Object (optional)\n Options.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of objects to store in an internal buffer\n before pausing streaming.\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n stream: ReadableStream\n Readable stream operating in \"objectMode\".\n\n Examples\n --------\n > function fcn( v ) { console.log( v ); };\n > var s = arrayStream.objectMode( [ 1, 2, 3 ] );\n > var o = inspectSinkStream.objectMode( fcn );\n > s.pipe( o );\n\n See Also\n --------\n circularArrayStream, iteratorStream, stridedArrayStream\n","arrayStream.factory":"\narrayStream.factory( [options] )\n Returns a function for creating readable streams from array-like objects.\n\n Parameters\n ----------\n options: Object (optional)\n Options.\n\n options.objectMode: boolean (optional)\n Specifies whether a stream should operate in \"objectMode\". Default:\n false.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of bytes to store in an internal buffer\n before pausing streaming.\n\n options.sep: string (optional)\n Separator used to join streamed data. This option is only applicable\n when a stream is not in \"objectMode\". Default: '\\n'.\n\n options.serialize: Function (optional)\n Serialization function. The default behavior is to serialize streamed\n values as JSON strings. This option is only applicable when a stream is\n not in \"objectMode\".\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n fcn: Function\n Function for creating readable streams.\n\n Examples\n --------\n > var opts = { 'objectMode': true, 'highWaterMark': 64 };\n > var createStream = arrayStream.factory( opts );","arrayStream.objectMode":"\narrayStream.objectMode( src[, options] )\n Returns an \"objectMode\" readable stream from an array-like object.\n\n In object mode, `null` is a reserved value. If an array contains `null`\n values (e.g., as a means to encode missing values), the stream will\n prematurely end. Consider an alternative encoding or filter `null` values\n prior to invocation.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Source value.\n\n options: Object (optional)\n Options.\n\n options.encoding: string|null (optional)\n Specifies how Buffer objects should be decoded to strings. Default:\n null.\n\n options.highWaterMark: integer (optional)\n Specifies the maximum number of objects to store in an internal buffer\n before pausing streaming.\n\n options.dir: integer (optional)\n Iteration direction. If set to `-1`, a stream iterates over elements\n from right-to-left. Default: 1.\n\n Returns\n -------\n stream: ReadableStream\n Readable stream operating in \"objectMode\".\n\n Examples\n --------\n > function fcn( v ) { console.log( v ); };\n > var s = arrayStream.objectMode( [ 1, 2, 3 ] );\n > var o = inspectSinkStream.objectMode( fcn );\n > s.pipe( o );\n\n See Also\n --------\n circularArrayStream, iteratorStream, stridedArrayStream","arrayview2iterator":"\narrayview2iterator( src[, begin[, end]][, mapFcn[, thisArg]] )\n Returns an iterator which iterates over the elements of an array-like object\n view.\n\n When invoked, an input function is provided four arguments:\n\n - value: iterated value\n - index: iterated value index\n - n: iteration count (zero-based)\n - src: source array-like object\n\n If an environment supports Symbol.iterator, the returned iterator is\n iterable.\n\n If an environment supports Symbol.iterator, the function explicitly does not\n invoke an array's `@@iterator` method, regardless of whether this method is\n defined. To convert an array to an implementation defined iterator, invoke\n this method directly.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Array-like object from which to create the iterator.\n\n begin: integer (optional)\n Starting index (inclusive). When negative, determined relative to the\n last element. Default: 0.\n\n end: integer (optional)\n Ending index (non-inclusive). When negative, determined relative to the\n last element. Default: src.length.\n\n mapFcn: Function (optional)\n Function to invoke for each iterated value.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n iterator: Object\n Iterator.\n\n iterator.next(): Function\n Returns an iterator protocol-compliant object containing the next\n iterated value (if one exists) and a boolean flag indicating whether the\n iterator is finished.\n\n iterator.return( [value] ): Function\n Finishes an iterator and returns a provided value.\n\n Examples\n --------\n > var it = arrayview2iterator( [ 1, 2, 3, 4 ], 1, 3 );\n > var v = it.next().value\n 2\n > v = it.next().value\n 3\n\n See Also\n --------\n iterator2array, array2iterator, stridedarray2iterator, arrayview2iteratorRight\n","arrayview2iteratorRight":"\narrayview2iteratorRight( src[, begin[, end]][, mapFcn[, thisArg]] )\n Returns an iterator which iterates from right to left over the elements of\n an array-like object view.\n\n When invoked, an input function is provided four arguments:\n\n - value: iterated value\n - index: iterated value index\n - n: iteration count (zero-based)\n - src: source array-like object\n\n If an environment supports Symbol.iterator, the returned iterator is\n iterable.\n\n If an environment supports Symbol.iterator, the function explicitly does not\n invoke an array's `@@iterator` method, regardless of whether this method is\n defined. To convert an array to an implementation defined iterator, invoke\n this method directly.\n\n Parameters\n ----------\n src: ArrayLikeObject\n Array-like object from which to create the iterator.\n\n begin: integer (optional)\n Starting index (inclusive). When negative, determined relative to the\n last element. Default: 0.\n\n end: integer (optional)\n Ending index (non-inclusive). When negative, determined relative to the\n last element. Default: src.length.\n\n mapFcn: Function (optional)\n Function to invoke for each iterated value.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n iterator: Object\n Iterator.\n\n iterator.next(): Function\n Returns an iterator protocol-compliant object containing the next\n iterated value (if one exists) and a boolean flag indicating whether the\n iterator is finished.\n\n iterator.return( [value] ): Function\n Finishes an iterator and returns a provided value.\n\n Examples\n --------\n > var it = arrayview2iteratorRight( [ 1, 2, 3, 4 ], 1, 3 );\n > var v = it.next().value\n 3\n > v = it.next().value\n 2\n\n See Also\n --------\n iterator2array, array2iteratorRight, stridedarray2iterator, arrayview2iterator\n","aslice":"\naslice( x[, start[, end]] )\n Returns a shallow copy of a portion of an array.\n\n If provided an array-like object having a `slice` method, the function\n defers execution to that method and assumes that the method has the\n following signature:\n\n x.slice( start, end )\n\n If provided an array-like object without a `slice` method, the function\n copies input array elements to a new generic array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n start: integer (optional)\n Starting index (inclusive). Default: 0.\n\n end: integer (optional)\n Ending index (exclusive). Default: x.length.\n\n Returns\n -------\n out: Array|TypedArray\n Output array.\n\n Examples\n --------\n > var out = aslice( [ 1, 2, 3, 4 ] )\n [ 1, 2, 3, 4 ]\n > out = aslice( [ 1, 2, 3, 4 ], 1 )\n [ 2, 3, 4 ]\n > out = aslice( [ 1, 2, 3, 4 ], 1, 3 )\n [ 2, 3 ]\n\n See Also\n --------\n atake\n","AsyncIteratorSymbol":"\nAsyncIteratorSymbol\n Async iterator symbol.\n\n This symbol specifies the default async iterator for an object.\n\n The symbol is only supported in ES2018+ environments. For non-supporting\n environments, the value is `null`.\n\n Examples\n --------\n > var s = AsyncIteratorSymbol\n\n See Also\n --------\n Symbol, IteratorSymbol\n","atake":"\natake( x, indices[, options] )\n Takes elements from an array.\n\n If `indices` is an empty array, the function returns an empty array.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n indices: ArrayLikeObject\n List of element indices.\n\n options: Object (optional)\n Function options.\n\n options.mode: string (optional)\n Specifies how to handle an index outside the interval [0, max], where\n `max` is the maximum possible array index. If equal to 'throw', the\n function throws an error. If equal to 'normalize', the function throws\n an error if provided an out-of-bounds normalized index. If equal to\n 'wrap', the function wraps around an index using modulo arithmetic. If\n equal to 'clamp', the function sets an index to either 0 (minimum index)\n or the maximum index. Default: 'normalize'.\n\n Returns\n -------\n out: Array|TypedArray\n Output array.\n\n Examples\n --------\n > var x = [ 1, 2, 3, 4 ];\n > var y = atake( x, [ 1, 3 ] )\n [ 2, 4 ]\n\n See Also\n --------\n aput, aslice\n","azeros":"\nazeros( length[, dtype] )\n Returns a zero-filled array having a specified length.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - int32: 32-bit two's complement signed integers\n - uint32: 32-bit unsigned integers\n - int16: 16-bit two's complement signed integers\n - uint16: 16-bit unsigned integers\n - int8: 8-bit two's complement signed integers\n - uint8: 8-bit unsigned integers\n - uint8c: 8-bit unsigned integers clamped to 0-255\n - generic: generic JavaScript values\n\n The default array data type is `float64`.\n\n Parameters\n ----------\n length: integer\n Array length.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = azeros( 2 )\n [ 0.0, 0.0 ]\n > arr = azeros( 2, 'float32' )\n [ 0.0, 0.0 ]\n\n See Also\n --------\n aempty, afull, anans, aones, azerosLike, ndzeros\n","azerosLike":"\nazerosLike( x[, dtype] )\n Returns a zero-filled array having the same length and data type as a\n provided input array.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - int32: 32-bit two's complement signed integers\n - uint32: 32-bit unsigned integers\n - int16: 16-bit two's complement signed integers\n - uint16: 16-bit unsigned integers\n - int8: 8-bit two's complement signed integers\n - uint8: 8-bit unsigned integers\n - uint8c: 8-bit unsigned integers clamped to 0-255\n - generic: generic JavaScript values\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var x = new Float64Array( 2 );\n > var y = azerosLike( x )\n [ 0.0, 0.0 ]\n > y = azerosLike( x, 'float32' )\n [ 0.0, 0.0 ]\n\n See Also\n --------\n aemptyLike, afullLike, anansLike, aonesLike, azeros, ndzerosLike\n","azeroTo":"\nazeroTo( n[, dtype] )\n Generates a linearly spaced numeric array whose elements increment by 1\n starting from zero.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - int32: 32-bit two's complement signed integers\n - uint32: 32-bit unsigned integers\n - int16: 16-bit two's complement signed integers\n - uint16: 16-bit unsigned integers\n - int8: 8-bit two's complement signed integers\n - uint8: 8-bit unsigned integers\n - uint8c: 8-bit unsigned integers clamped to 0-255\n - generic: generic JavaScript values\n\n The default array data type is `float64`.\n\n If `n` is equal to zero, the function returns an empty array.\n\n Parameters\n ----------\n n: integer\n Number of elements.\n\n dtype: string (optional)\n Data type. Default: 'float64'.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = azeroTo( 2 )\n [ 0.0, 1.0 ]\n > arr = azeroTo( 2, 'float32' )\n [ 0.0, 1.0 ]\n\n See Also\n --------\n aempty, afull, aoneTo, azeroToLike, azeros\n","azeroToLike":"\nazeroToLike( x[, dtype] )\n Generates a linearly spaced numeric array whose elements increment by 1\n starting from zero and having the same length and data type as a provided\n input array.\n\n The function supports the following data types:\n\n - float64: double-precision floating-point numbers (IEEE 754)\n - float32: single-precision floating-point numbers (IEEE 754)\n - complex128: double-precision complex floating-point numbers\n - complex64: single-precision complex floating-point numbers\n - int32: 32-bit two's complement signed integers\n - uint32: 32-bit unsigned integers\n - int16: 16-bit two's complement signed integers\n - uint16: 16-bit unsigned integers\n - int8: 8-bit two's complement signed integers\n - uint8: 8-bit unsigned integers\n - uint8c: 8-bit unsigned integers clamped to 0-255\n - generic: generic JavaScript values\n\n Parameters\n ----------\n x: TypedArray|Array\n Input array.\n\n dtype: string (optional)\n Data type. If not provided, the output array data type is inferred from\n the input array.\n\n Returns\n -------\n out: TypedArray|Array\n Output array.\n\n Examples\n --------\n > var arr = azeroToLike( [ 0, 0 ] )\n [ 0, 1 ]\n > arr = azeroToLike( [ 0, 0 ], 'float32' )\n [ 0.0, 1.0 ]\n\n See Also\n --------\n aemptyLike, afullLike, anansLike, aoneToLike, aonesLike, azeroTo, azerosLike\n","bartlettTest":"\nbartlettTest( ...x[, options] )\n Computes Bartlett’s test for equal variances.\n\n Parameters\n ----------\n x: ...Array\n Measured values.\n\n options: Object (optional)\n Options.\n\n options.alpha: number (optional)\n Number in the interval `[0,1]` giving the significance level of the\n hypothesis test. Default: `0.05`.\n\n options.groups: Array (optional)\n Array of group indicators.\n\n Returns\n -------\n out: Object\n Test result object.\n\n out.alpha: number\n Significance level.\n\n out.rejected: boolean\n Test decision.\n\n out.pValue: number\n p-value of the test.\n\n out.statistic: number\n Value of test statistic.\n\n out.method: string\n Name of test.\n\n out.df: Object\n Degrees of freedom.\n\n out.print: Function\n Function to print formatted output.\n\n Examples\n --------\n // Data from Hollander & Wolfe (1973), p. 116:\n > var x = [ 2.9, 3.0, 2.5, 2.6, 3.2 ];\n > var y = [ 3.8, 2.7, 4.0, 2.4 ];\n > var z = [ 2.8, 3.4, 3.7, 2.2, 2.0 ];\n\n > var out = bartlettTest( x, y, z )\n\n > var arr = [ 2.9, 3.0, 2.5, 2.6, 3.2,\n ... 3.8, 2.7, 4.0, 2.4,\n ... 2.8, 3.4, 3.7, 2.2, 2.0\n ... ];\n > var groups = [\n ... 'a', 'a', 'a', 'a', 'a',\n ... 'b', 'b', 'b', 'b',\n ... 'c', 'c', 'c', 'c', 'c'\n ... ];\n > out = bartlettTest( arr, { 'groups': groups } )\n\n See Also\n --------\n vartest, leveneTest\n","base.abs":"\nbase.abs( x )\n Computes the absolute value of a double-precision floating-point number `x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Absolute value.\n\n Examples\n --------\n > var y = base.abs( -1.0 )\n 1.0\n > y = base.abs( 2.0 )\n 2.0\n > y = base.abs( 0.0 )\n 0.0\n > y = base.abs( -0.0 )\n 0.0\n > y = base.abs( NaN )\n NaN\n\n See Also\n --------\n base.abs2, base.absf, base.labs\n","base.abs2":"\nbase.abs2( x )\n Computes the squared absolute value of a double-precision floating-point\n `x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Squared absolute value.\n\n Examples\n --------\n > var y = base.abs2( -1.0 )\n 1.0\n > y = base.abs2( 2.0 )\n 4.0\n > y = base.abs2( 0.0 )\n 0.0\n > y = base.abs2( -0.0 )\n 0.0\n > y = base.abs2( NaN )\n NaN\n\n See Also\n --------\n base.abs, base.abs2f\n","base.abs2f":"\nbase.abs2f( x )\n Computes the squared absolute value of a single-precision floating-point\n `x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Squared absolute value.\n\n Examples\n --------\n > var y = base.abs2f( -1.0 )\n 1.0\n > y = base.abs2f( 2.0 )\n 4.0\n > y = base.abs2f( 0.0 )\n 0.0\n > y = base.abs2f( -0.0 )\n 0.0\n > y = base.abs2f( NaN )\n NaN\n\n See Also\n --------\n base.abs2, base.absf\n","base.absdiff":"\nbase.absdiff( x, y )\n Computes the absolute difference.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: number\n Absolute difference.\n\n Examples\n --------\n > var d = base.absdiff( 2.0, 5.0 )\n 3.0\n > d = base.absdiff( -1.0, 3.14 )\n ~4.14\n > d = base.absdiff( 10.1, -2.05 )\n ~12.15\n > d = base.absdiff( -0.0, 0.0 )\n +0.0\n > d = base.absdiff( NaN, 5.0 )\n NaN\n > d = base.absdiff( PINF, NINF )\n Infinity\n > d = base.absdiff( PINF, PINF )\n NaN\n\n See Also\n --------\n base.reldiff, base.epsdiff\n","base.absf":"\nbase.absf( x )\n Computes the absolute value of a single-precision floating-point number `x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Absolute value.\n\n Examples\n --------\n > var y = base.absf( -1.0 )\n 1.0\n > y = base.absf( 2.0 )\n 2.0\n > y = base.absf( 0.0 )\n 0.0\n > y = base.absf( -0.0 )\n 0.0\n > y = base.absf( NaN )\n NaN\n\n See Also\n --------\n base.abs, base.abs2f, base.labs\n","base.acartesianPower":"\nbase.acartesianPower( x, n )\n Returns the Cartesian power.\n\n If provided an empty array, the function returns an empty array.\n\n If `n` is less than or equal to zero, the function returns an empty array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n n: integer\n Power.\n\n Returns\n -------\n out: Array\n Cartesian product.\n\n Examples\n --------\n > var x = [ 1, 2 ];\n > var out = base.acartesianPower( x, 2 )\n [ [ 1, 1 ], [ 1, 2 ], [ 2, 1 ], [ 2, 2 ] ]\n\n See Also\n --------\n acartesianPower, base.acartesianProduct, base.acartesianSquare\n","base.acartesianProduct":"\nbase.acartesianProduct( x1, x2 )\n Returns the Cartesian product.\n\n If provided one or more empty arrays, the function returns an empty array.\n\n Parameters\n ----------\n x1: ArrayLikeObject\n First input array.\n\n x2: ArrayLikeObject\n Second input array.\n\n Returns\n -------\n out: Array\n Cartesian product.\n\n Examples\n --------\n > var x1 = [ 1, 2 ];\n > var x2 = [ 3, 4 ];\n > var out = base.acartesianProduct( x1, x2 )\n [ [ 1, 3 ], [ 1, 4 ], [ 2, 3 ], [ 2, 4 ] ]\n\n See Also\n --------\n acartesianProduct, base.acartesianPower, base.acartesianSquare\n","base.acartesianSquare":"\nbase.acartesianSquare( x )\n Returns the Cartesian square.\n\n If provided an empty array, the function returns an empty array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n Returns\n -------\n out: Array\n Cartesian product.\n\n Examples\n --------\n > var x = [ 1, 2 ];\n > var out = base.acartesianSquare( x )\n [ [ 1, 1 ], [ 1, 2 ], [ 2, 1 ], [ 2, 2 ] ]\n\n See Also\n --------\n acartesianSquare, base.acartesianPower, base.acartesianProduct\n","base.acos":"\nbase.acos( x )\n Compute the arccosine of a double-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arccosine (in radians).\n\n Examples\n --------\n > var y = base.acos( 1.0 )\n 0.0\n > y = base.acos( 0.707 )\n ~0.7855\n > y = base.acos( NaN )\n NaN\n\n See Also\n --------\n base.acosh, base.asin, base.atan\n","base.acosd":"\nbase.acosd( x )\n Computes the arccosine (in degrees) of a double-precision floating-point \n number.\n\n If `|x| > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arccosine (in degrees).\n\n Examples\n --------\n > var y = base.acosd( 0.0 )\n 90.0\n > y = base.acosd( PI/6.0 )\n ~58.43\n > y = base.acosd( NaN )\n NaN\n\n See Also\n --------\n base.acos, base.acosh, base.asind, base.atand\n","base.acosf":"\nbase.acosf( x )\n Computes the arccosine of a single-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arccosine (in radians).\n\n Examples\n --------\n > var y = base.acosf( 1.0 )\n 0.0\n > y = base.acosf( 0.707 )\n ~0.7855\n > y = base.acosf( NaN )\n NaN\n\n See Also\n --------\n base.acos, base.acosh, base.asinf, base.atanf\n","base.acosh":"\nbase.acosh( x )\n Computes the hyperbolic arccosine of a double-precision floating-point\n number.\n\n If `x < 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Hyperbolic arccosine.\n\n Examples\n --------\n > var y = base.acosh( 1.0 )\n 0.0\n > y = base.acosh( 2.0 )\n ~1.317\n > y = base.acosh( NaN )\n NaN\n\n See Also\n --------\n base.acos, base.asinh, base.atanh\n","base.acot":"\nbase.acot( x )\n Computes the inverse cotangent of a double-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse cotangent (in radians).\n\n Examples\n --------\n > var y = base.acot( 2.0 )\n ~0.4636\n > y = base.acot( 0.0 )\n ~1.5708\n > y = base.acot( 0.5 )\n ~1.1071\n > y = base.acot( 1.0 )\n ~0.7854\n > y = base.acot( NaN )\n NaN\n\n See Also\n --------\n base.acoth, base.atan, base.cot\n","base.acotd":"\nbase.acotd( x )\n Computes the arccotangent (in degrees) of a double-precision floating-point\n number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arccotangent (in degrees).\n\n Examples\n --------\n > var y = base.acotd( 0.0 )\n 90.0\n > y = base.acotd( PI/6.0 )\n ~62.36\n > y = base.acotd( NaN )\n NaN\n\n See Also\n --------\n base.acot, base.acoth, base.atand, base.cotd\n","base.acotf":"\nbase.acotf( x )\n Computes the inverse cotangent of a single-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse cotangent (in radians).\n\n Examples\n --------\n > var y = base.acotf( 2.0 )\n ~0.4636\n > y = base.acotf( 0.0 )\n ~1.5708\n > y = base.acotf( 0.5 )\n ~1.1071\n > y = base.acotf( 1.0 )\n ~0.7854\n > y = base.acotf( NaN )\n NaN\n\n See Also\n --------\n base.acot, base.acoth, base.atanf\n","base.acoth":"\nbase.acoth( x )\n Computes the inverse hyperbolic cotangent of a double-precision floating-\n point number.\n\n The domain of the inverse hyperbolic cotangent is the union of the intervals\n (-inf,-1] and [1,inf).\n\n If provided a value on the open interval (-1,1), the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse hyperbolic cotangent.\n\n Examples\n --------\n > var y = base.acoth( 2.0 )\n ~0.5493\n > y = base.acoth( 0.0 )\n NaN\n > y = base.acoth( 0.5 )\n NaN\n > y = base.acoth( 1.0 )\n Infinity\n > y = base.acoth( NaN )\n NaN\n\n See Also\n --------\n base.acosh, base.acot, base.asinh, base.atanh\n","base.acovercos":"\nbase.acovercos( x )\n Computes the inverse coversed cosine.\n\n The inverse coversed cosine is defined as `asin(1+x)`.\n\n If `x < -2`, `x > 0`, or `x` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse coversed cosine.\n\n Examples\n --------\n > var y = base.acovercos( -1.5 )\n ~-0.5236\n > y = base.acovercos( -0.0 )\n ~1.5708\n\n See Also\n --------\n base.acoversin, base.avercos, base.covercos, base.vercos\n","base.acoversin":"\nbase.acoversin( x )\n Computes the inverse coversed sine.\n\n The inverse coversed sine is defined as `asin(1-x)`.\n\n If `x < 0`, `x > 2`, or `x` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse coversed sine.\n\n Examples\n --------\n > var y = base.acoversin( 1.5 )\n ~-0.5236\n > y = base.acoversin( 0.0 )\n ~1.5708\n\n See Also\n --------\n base.acovercos, base.aversin, base.coversin, base.versin\n","base.acsc":"\nbase.acsc( x )\n Computes the arccosecant of a number.\n\n If `|x| < 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arccosecant (in radians).\n\n Examples\n --------\n > var y = base.acsc( 1.0 )\n ~1.57\n > y = base.acsc( PI )\n ~0.32\n > y = base.acsc( -PI )\n ~-0.32\n > y = base.acsc( NaN )\n NaN\n\n See Also\n --------\n base.acot, base.acsch, base.asec, base.asin, base.csc\n","base.acscd":"\nbase.acscd( x )\n Computes the arccosecant of (in degrees) a double-precision floating-point\n number.\n\n If `x` does not satisy `x >= 1` or `x <= -1`, the function returns NaN.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arccosecant (in degrees).\n\n Examples\n --------\n > var y = base.acscd( 0.0 )\n NaN\n > y = base.acscd( PI/6.0 )\n NaN\n > y = base.acscd( 1 )\n 90.0\n > y = base.acscd( NaN )\n NaN\n\n See Also\n --------\n base.acsc, base.acsch, base.asecd, base.asind, base.cscd\n","base.acscdf":"\nbase.acscdf( x )\n Computes the arccosecant (in degrees) of a single-precision floating-point\n number.\n\n If `x` does not satisy `x >= 1` or `x <= -1`, the function returns NaN.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arccosecant (in degrees).\n\n Examples\n --------\n > var y = base.acscdf( 0.0 )\n NaN\n > y = base.acscdf( 3.1415927410125732 / 6.0 )\n NaN\n > y = base.acscdf( 1.0 )\n 90.0\n > y = base.acscdf( NaN )\n NaN\n\n See Also\n --------\n base.acsc, base.acsch, base.asecdf, base.asindf\n","base.acscf":"\nbase.acscf( x )\n Computes the arccosecant of a single-precision floating-point number.\n\n If `|x| < 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arccosecant (in radians).\n\n Examples\n --------\n > var y = base.acscf( 1.0 )\n ~1.57\n > y = base.acscf( 3.141592653589793 )\n ~0.32\n > y = base.acscf( -3.141592653589793 )\n ~-0.32\n > y = base.acscf( NaN )\n NaN\n\n See Also\n --------\n base.acsc, base.acsch, base.asecf, base.asinf\n","base.acsch":"\nbase.acsch( x )\n Computes the hyperbolic arccosecant of a number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Hyperbolic arccosecant.\n\n Examples\n --------\n > var y = base.acsch( 0.0 )\n Infinity\n > y = base.acsch( -1.0 )\n ~-0.881\n > y = base.acsch( NaN )\n NaN\n\n See Also\n --------\n base.acoth, base.acsc, base.asech, base.asinh, base.csc, base.csch\n","base.add":"\nbase.add( x, y )\n Computes the sum of two double-precision floating-point numbers `x` and `y`.\n\n Parameters\n ----------\n x: number\n First input value.\n\n y: number\n Second input value.\n\n Returns\n -------\n z: number\n Sum.\n\n Examples\n --------\n > var v = base.add( -1.0, 5.0 )\n 4.0\n > v = base.add( 2.0, 5.0 )\n 7.0\n > v = base.add( 0.0, 5.0 )\n 5.0\n > v = base.add( -0.0, 0.0 )\n 0.0\n > v = base.add( NaN, NaN )\n NaN\n\n See Also\n --------\n base.div, base.mul, base.sub\n","base.add3":"\nbase.add3( x, y, z )\n Computes the sum of three double-precision floating-point numbers.\n\n Parameters\n ----------\n x: number\n First input value.\n\n y: number\n Second input value.\n\n z: number\n Third input value.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n > var v = base.add3( -1.0, 5.0, 2.0 )\n 6.0\n > v = base.add3( 2.0, 5.0, 2.0 )\n 9.0\n > v = base.add3( 0.0, 5.0, 2.0 )\n 7.0\n > v = base.add3( -0.0, 0.0, -0.0 )\n 0.0\n > v = base.add3( NaN, NaN, NaN )\n NaN\n\n See Also\n --------\n base.add\n","base.add4":"\nbase.add4( x, y, z, w )\n Computes the sum of four double-precision floating-point numbers.\n\n Parameters\n ----------\n x: number\n First input value.\n\n y: number\n Second input value.\n\n z: number\n Third input value.\n\n w: number\n Fourth input value.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n > var v = base.add4( -1.0, 5.0, 2.0, -3.0 )\n 3.0\n > v = base.add4( 2.0, 5.0, 2.0, -3.0 )\n 6.0\n > v = base.add4( 0.0, 5.0, 2.0, -3.0 )\n 4.0\n > v = base.add4( -0.0, 0.0, -0.0, -0.0 )\n 0.0\n > v = base.add4( NaN, NaN, NaN, NaN )\n NaN\n\n See Also\n --------\n base.add\n","base.add5":"\nbase.add5( x, y, z, w, u )\n Computes the sum of five double-precision floating-point numbers.\n\n Parameters\n ----------\n x: number\n First input value.\n\n y: number\n Second input value.\n\n z: number\n Third input value.\n\n w: number\n Fourth input value.\n\n u: number\n Fifth input value.\n\n Returns\n -------\n out: number\n Sum.\n\n Examples\n --------\n > var v = base.add5( -1.0, 5.0, 2.0, -3.0, 4.0 )\n 7.0\n > v = base.add5( 2.0, 5.0, 2.0, -3.0, 4.0 )\n 10.0\n > v = base.add5( 0.0, 5.0, 2.0, -3.0, 4.0 )\n 8.0\n > v = base.add5( -0.0, 0.0, -0.0, -0.0, -0.0 )\n 0.0\n > v = base.add5( NaN, NaN, NaN, NaN, NaN )\n NaN\n\n See Also\n --------\n base.add\n","base.addf":"\nbase.addf( x, y )\n Computes the sum of two single-precision floating-point numbers `x` and `y`.\n\n Parameters\n ----------\n x: number\n First input value.\n\n y: number\n Second input value.\n\n Returns\n -------\n z: number\n Sum.\n\n Examples\n --------\n > var v = base.addf( -1.0, 5.0 )\n 4.0\n > v = base.addf( 2.0, 5.0 )\n 7.0\n > v = base.addf( 0.0, 5.0 )\n 5.0\n > v = base.addf( -0.0, 0.0 )\n 0.0\n > v = base.addf( NaN, NaN )\n NaN\n\n See Also\n --------\n base.add, base.divf, base.mulf, base.subf\n","base.afilled":"\nbase.afilled( value, len )\n Returns a filled \"generic\" array.\n\n Parameters\n ----------\n value: any\n Fill value.\n\n len: integer\n Array length.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afilled( 0.0, 3 )\n [ 0.0, 0.0, 0.0 ]\n\n","base.afilled2d":"\nbase.afilled2d( value, shape )\n Returns a filled two-dimensional nested array.\n\n Parameters\n ----------\n value: any\n Fill value.\n\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afilled2d( 0.0, [ 1, 3 ] )\n [ [ 0.0, 0.0, 0.0 ] ]\n\n","base.afilled2dBy":"\nbase.afilled2dBy( shape, clbk[, thisArg] )\n Returns a filled two-dimensional nested array according to a provided\n callback function.\n\n The callback function is provided one argument:\n\n - indices: current array element indices.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function clbk() { return 1.0; };\n > var out = base.afilled2dBy( [ 1, 3 ], clbk )\n [ [ 1.0, 1.0, 1.0 ] ]\n\n See Also\n --------\n base.afilled2d\n","base.afilled3d":"\nbase.afilled3d( value, shape )\n Returns a filled three-dimensional nested array.\n\n Parameters\n ----------\n value: any\n Fill value.\n\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afilled3d( 0.0, [ 1, 1, 3 ] )\n [ [ [ 0.0, 0.0, 0.0 ] ] ]\n\n","base.afilled3dBy":"\nbase.afilled3dBy( shape, clbk[, thisArg] )\n Returns a filled three-dimensional nested array according to a provided\n callback function.\n\n The callback function is provided one argument:\n\n - indices: current array element indices.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function clbk() { return 1.0; };\n > var out = base.afilled3dBy( [ 1, 1, 3 ], clbk )\n [ [ [ 1.0, 1.0, 1.0 ] ] ]\n\n See Also\n --------\n base.afilled3d\n","base.afilled4d":"\nbase.afilled4d( value, shape )\n Returns a filled four-dimensional nested array.\n\n Parameters\n ----------\n value: any\n Fill value.\n\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afilled4d( 0.0, [ 1, 1, 1, 3 ] )\n [ [ [ [ 0.0, 0.0, 0.0 ] ] ] ]\n\n","base.afilled4dBy":"\nbase.afilled4dBy( shape, clbk[, thisArg] )\n Returns a filled four-dimensional nested array according to a provided\n callback function.\n\n The callback function is provided one argument:\n\n - indices: current array element indices.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function clbk() { return 1.0; };\n > var out = base.afilled4dBy( [ 1, 1, 1, 3 ], clbk )\n [ [ [ [ 1.0, 1.0, 1.0 ] ] ] ]\n\n See Also\n --------\n base.afilled4d\n","base.afilled5d":"\nbase.afilled5d( value, shape )\n Returns a filled five-dimensional nested array.\n\n Parameters\n ----------\n value: any\n Fill value.\n\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afilled5d( 0.0, [ 1, 1, 1, 1, 3 ] )\n [ [ [ [ [ 0.0, 0.0, 0.0 ] ] ] ] ]\n\n","base.afilled5dBy":"\nbase.afilled5dBy( shape, clbk[, thisArg] )\n Returns a filled five-dimensional nested array according to a provided\n callback function.\n\n The callback function is provided one argument:\n\n - indices: current array element indices.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function clbk() { return 1.0; };\n > var out = base.afilled5dBy( [ 1, 1, 1, 1, 3 ], clbk )\n [ [ [ [ [ 1.0, 1.0, 1.0 ] ] ] ] ]\n\n See Also\n --------\n base.afilled5d\n","base.afilledBy":"\nbase.afilledBy( len, clbk[, thisArg] )\n Returns a filled \"generic\" array according to a provided callback function.\n\n Parameters\n ----------\n len: integer\n Array length.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function clbk() { return 1.0; };\n > var out = base.afilledBy( 3, clbk )\n [ 1.0, 1.0, 1.0 ]\n\n See Also\n --------\n base.afilled\n","base.afillednd":"\nbase.afillednd( value, shape )\n Returns a filled n-dimensional nested array.\n\n Parameters\n ----------\n value: any\n Fill value.\n\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afillednd( 0.0, [ 1, 3 ] )\n [ [ 0.0, 0.0, 0.0 ] ]\n\n","base.afilledndBy":"\nbase.afilledndBy( shape, clbk[, thisArg] )\n Returns a filled n-dimensional nested array according to a callback\n function.\n\n The callback function is provided one argument:\n\n - indices: current array element indices.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function clbk() { return 1.0; };\n > var out = base.afilledndBy( [ 1, 3 ], clbk )\n [ [ 1.0, 1.0, 1.0 ] ]\n\n See Also\n --------\n base.afillednd\n","base.afilter":"\nbase.afilter( x, predicate[, thisArg] )\n Returns a shallow copy of an array containing only those elements which pass\n a test implemented by a predicate function.\n\n The predicate function is provided three arguments:\n\n - value: current array element.\n - index: current array element index.\n - arr: the input array.\n\n If provided an array-like object having a `filter` method , the function\n defers execution to that method and assumes that the method has the\n following signature:\n\n x.filter( predicate, thisArg )\n\n If provided an array-like object without a `filter` method, the function\n performs a linear scan and always returns a generic array.\n\n Parameters\n ----------\n x: Array|TypedArray|Object\n Input array.\n\n predicate: Function\n Predicate function.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array.\n\n Examples\n --------\n > function f( v ) { return ( v > 0 ); };\n > var x = [ 1, -2, -3, 4 ];\n > var out = base.afilter( x, f )\n [ 1, 4 ]\n\n","base.afirst":"\nbase.afirst( arr )\n Returns the first element of an array-like object.\n\n Parameters\n ----------\n arr: ArrayLikeObject\n Input array.\n\n Returns\n -------\n out: any\n First element.\n\n Examples\n --------\n > var out = base.afirst( [ 1, 2, 3 ] )\n 1\n\n","base.aflatten":"\nbase.aflatten( x, shape, colexicographic )\n Flattens an n-dimensional nested array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = base.aflatten( x, [ 2, 2 ], false )\n [ 1, 2, 3, 4 ]\n > out = base.aflatten( x, [ 2, 2 ], true )\n [ 1, 3, 2, 4 ]\n\n\nbase.aflatten.assign( x, shape, colexicographic, out, stride, offset )\n Flattens an n-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten.assign( x, [ 2, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten.assign( x, [ 2, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflattenBy\n","base.aflatten.assign":"\nbase.aflatten.assign( x, shape, colexicographic, out, stride, offset )\n Flattens an n-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten.assign( x, [ 2, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten.assign( x, [ 2, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflattenBy","base.aflatten2d":"\nbase.aflatten2d( x, shape, colexicographic )\n Flattens a two-dimensional nested array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = base.aflatten2d( x, [ 2, 2 ], false )\n [ 1, 2, 3, 4 ]\n > out = base.aflatten2d( x, [ 2, 2 ], true )\n [ 1, 3, 2, 4 ]\n\n\nbase.aflatten2d.assign( x, shape, colexicographic, out, stride, offset )\n Flattens a two-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten2d.assign( x, [ 2, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten2d.assign( x, [ 2, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflatten2dBy\n","base.aflatten2d.assign":"\nbase.aflatten2d.assign( x, shape, colexicographic, out, stride, offset )\n Flattens a two-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten2d.assign( x, [ 2, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten2d.assign( x, [ 2, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflatten2dBy","base.aflatten2dBy":"\nbase.aflatten2dBy( x, shape, colex, clbk[, thisArg] )\n Flattens a two-dimensional nested array according to a callback function.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = base.aflatten2dBy( x, [ 2, 2 ], false, fcn )\n [ 2, 4, 6, 8 ]\n > out = base.aflatten2dBy( x, [ 2, 2 ], true, fcn )\n [ 2, 6, 4, 8 ]\n\n\nbase.aflatten2dBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens a two-dimensional nested array according to a callback function\n and assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten2dBy.assign( x, [ 2, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten2dBy.assign( x, [ 2, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten2d\n","base.aflatten2dBy.assign":"\nbase.aflatten2dBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens a two-dimensional nested array according to a callback function\n and assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten2dBy.assign( x, [ 2, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten2dBy.assign( x, [ 2, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten2d","base.aflatten3d":"\nbase.aflatten3d( x, shape, colexicographic )\n Flattens a three-dimensional nested array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > var x = [ [ [ 1, 2 ] ], [ [ 3, 4 ] ] ];\n > var out = base.aflatten3d( x, [ 2, 1, 2 ], false )\n [ 1, 2, 3, 4 ]\n > out = base.aflatten3d( x, [ 2, 1, 2 ], true )\n [ 1, 3, 2, 4 ]\n\n\nbase.aflatten3d.assign( x, shape, colexicographic, out, stride, offset )\n Flattens a three-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ [ 1, 2 ] ], [ [ 3, 4 ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten3d.assign( x, [ 2, 1, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten3d.assign( x, [ 2, 1, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflatten3dBy\n","base.aflatten3d.assign":"\nbase.aflatten3d.assign( x, shape, colexicographic, out, stride, offset )\n Flattens a three-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ [ 1, 2 ] ], [ [ 3, 4 ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten3d.assign( x, [ 2, 1, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten3d.assign( x, [ 2, 1, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflatten3dBy","base.aflatten3dBy":"\nbase.aflatten3dBy( x, shape, colex, clbk[, thisArg] )\n Flattens a three-dimensional nested array according to a callback function.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ [ 1, 2 ] ], [ [ 3, 4 ] ] ];\n > var out = base.aflatten3dBy( x, [ 2, 1, 2 ], false, fcn )\n [ 2, 4, 6, 8 ]\n > out = base.aflatten3dBy( x, [ 2, 1, 2 ], true, fcn )\n [ 2, 6, 4, 8 ]\n\n\nbase.aflatten3dBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens a three-dimensional nested array according to a callback function\n and assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ [ 1, 2 ] ], [ [ 3, 4 ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten3dBy.assign( x, [ 2, 1, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten3dBy.assign( x, [ 2, 1, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten3d\n","base.aflatten3dBy.assign":"\nbase.aflatten3dBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens a three-dimensional nested array according to a callback function\n and assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ [ 1, 2 ] ], [ [ 3, 4 ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten3dBy.assign( x, [ 2, 1, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten3dBy.assign( x, [ 2, 1, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten3d","base.aflatten4d":"\nbase.aflatten4d( x, shape, colexicographic )\n Flattens a four-dimensional nested array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > var x = [ [ [ [ 1, 2 ] ] ], [ [ [ 3, 4 ] ] ] ];\n > var out = base.aflatten4d( x, [ 2, 1, 1, 2 ], false )\n [ 1, 2, 3, 4 ]\n > out = base.aflatten4d( x, [ 2, 1, 1, 2 ], true )\n [ 1, 3, 2, 4 ]\n\n\nbase.aflatten4d.assign( x, shape, colexicographic, out, stride, offset )\n Flattens a four-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ [ [ 1, 2 ] ] ], [ [ [ 3, 4 ] ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten4d.assign( x, [ 2, 1, 1, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten4d.assign( x, [ 2, 1, 1, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflatten4dBy\n","base.aflatten4d.assign":"\nbase.aflatten4d.assign( x, shape, colexicographic, out, stride, offset )\n Flattens a four-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ [ [ 1, 2 ] ] ], [ [ [ 3, 4 ] ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten4d.assign( x, [ 2, 1, 1, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten4d.assign( x, [ 2, 1, 1, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflatten4dBy","base.aflatten4dBy":"\nbase.aflatten4dBy( x, shape, colex, clbk[, thisArg] )\n Flattens a four-dimensional nested array according to a callback function.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ [ [ 1, 2 ] ] ], [ [ [ 3, 4 ] ] ] ];\n > var out = base.aflatten4dBy( x, [ 2, 1, 1, 2 ], false, fcn )\n [ 2, 4, 6, 8 ]\n > out = base.aflatten4dBy( x, [ 2, 1, 1, 2 ], true, fcn )\n [ 2, 6, 4, 8 ]\n\n\nbase.aflatten4dBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens a four-dimensional nested array according to a callback function\n and assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ [ [ 1, 2 ] ] ], [ [ [ 3, 4 ] ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten4dBy.assign( x, [ 2, 1, 1, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten4dBy.assign( x, [ 2, 1, 1, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten4d\n","base.aflatten4dBy.assign":"\nbase.aflatten4dBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens a four-dimensional nested array according to a callback function\n and assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ [ [ 1, 2 ] ] ], [ [ [ 3, 4 ] ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten4dBy.assign( x, [ 2, 1, 1, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten4dBy.assign( x, [ 2, 1, 1, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten4d","base.aflatten5d":"\nbase.aflatten5d( x, shape, colexicographic )\n Flattens a five-dimensional nested array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > var x = [ [ [ [ [ 1, 2 ] ] ] ], [ [ [ [ 3, 4 ] ] ] ] ];\n > var out = base.aflatten5d( x, [ 2, 1, 1, 1, 2 ], false )\n [ 1, 2, 3, 4 ]\n > out = base.aflatten5d( x, [ 2, 1, 1, 1, 2 ], true )\n [ 1, 3, 2, 4 ]\n\n\nbase.aflatten5d.assign( x, shape, colexicographic, out, stride, offset )\n Flattens a five-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ [ [ [ 1, 2 ] ] ] ], [ [ [ [ 3, 4 ] ] ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten5d.assign( x, [ 2, 1, 1, 1, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten5d.assign( x, [ 2, 1, 1, 1, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflatten5dBy\n","base.aflatten5d.assign":"\nbase.aflatten5d.assign( x, shape, colexicographic, out, stride, offset )\n Flattens a five-dimensional nested array and assigns elements to a provided\n output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colexicographic: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var x = [ [ [ [ [ 1, 2 ] ] ] ], [ [ [ [ 3, 4 ] ] ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten5d.assign( x, [ 2, 1, 1, 1, 2 ], false, out, 1, 0 )\n [ 1, 2, 3, 4 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten5d.assign( x, [ 2, 1, 1, 1, 2 ], true, out, 1, 0 );\n > out\n [ 1, 3, 2, 4 ]\n\n See Also\n --------\n base.aflatten5dBy","base.aflatten5dBy":"\nbase.aflatten5dBy( x, shape, colex, clbk[, thisArg] )\n Flattens a five-dimensional nested array according to a callback function.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ [ [ [ 1, 2 ] ] ] ], [ [ [ [ 3, 4 ] ] ] ] ];\n > var out = base.aflatten5dBy( x, [ 2, 1, 1, 1, 2 ], false, fcn )\n [ 2, 4, 6, 8 ]\n > out = base.aflatten5dBy( x, [ 2, 1, 1, 1, 2 ], true, fcn )\n [ 2, 6, 4, 8 ]\n\n\nbase.aflatten5dBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens a five-dimensional nested array according to a callback function\n and assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ [ [ [ 1, 2 ] ] ] ], [ [ [ [ 3, 4 ] ] ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten5dBy.assign( x, [ 2, 1, 1, 1, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten5dBy.assign( x, [ 2, 1, 1, 1, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten5d\n","base.aflatten5dBy.assign":"\nbase.aflatten5dBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens a five-dimensional nested array according to a callback function\n and assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ [ [ [ 1, 2 ] ] ] ], [ [ [ [ 3, 4 ] ] ] ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflatten5dBy.assign( x, [ 2, 1, 1, 1, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflatten5dBy.assign( x, [ 2, 1, 1, 1, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten5d","base.aflattenBy":"\nbase.aflattenBy( x, shape, colex, clbk[, thisArg] )\n Flattens an n-dimensional nested array according to a callback function.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Flattened array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = base.aflattenBy( x, [ 2, 2 ], false, fcn )\n [ 2, 4, 6, 8 ]\n > out = base.aflattenBy( x, [ 2, 2 ], true, fcn )\n [ 2, 6, 4, 8 ]\n\n\nbase.aflattenBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens an n-dimensional nested array according to a callback function and\n assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflattenBy.assign( x, [ 2, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflattenBy.assign( x, [ 2, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten\n","base.aflattenBy.assign":"\nbase.aflattenBy.assign( x, shape, colex, out, stride, offset, clbk[, thisArg] )\n Flattens an n-dimensional nested array according to a callback function and\n assigns elements to a provided output array.\n\n The function assumes that all nested arrays have the same length (i.e., the\n input array is *not* a ragged array).\n\n The callback function is provided the following arguments:\n\n - value: nested array element.\n - indices: element indices (in lexicographic order).\n - arr: the input array.\n\n Parameters\n ----------\n x: Array\n Input array.\n\n shape: Array\n Array shape.\n\n colex: boolean\n Specifies whether to flatten array values in colexicographic order.\n\n out: Collection\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n clbk: Function\n Callback function.\n\n thisArg: any (optional)\n Callback execution context.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > function fcn( v ) { return v * 2; };\n > var x = [ [ 1, 2 ], [ 3, 4 ] ];\n > var out = [ 0, 0, 0, 0 ];\n > var v = base.aflattenBy.assign( x, [ 2, 2 ], false, out, 1, 0, fcn )\n [ 2, 4, 6, 8 ]\n > var bool = ( v === out )\n true\n > out = [ 0, 0, 0, 0 ];\n > base.aflattenBy.assign( x, [ 2, 2 ], true, out, 1, 0, fcn );\n > out\n [ 2, 6, 4, 8 ]\n\n See Also\n --------\n base.aflatten","base.afliplr2d":"\nbase.afliplr2d( x )\n Reverses the order of elements along the last dimension of a two-dimensional\n nested input array.\n\n The function does *not* perform a deep copy of nested array elements.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input nested array.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afliplr2d( [ [ 1, 2 ], [ 3, 4 ] ] )\n [ [ 2, 1 ], [ 4, 3 ] ]\n\n See Also\n --------\n base.afliplr3d, base.afliplr4d, base.afliplr5d\n","base.afliplr3d":"\nbase.afliplr3d( x )\n Reverses the order of elements along the last dimension of a three-\n dimensional nested input array.\n\n The function does *not* perform a deep copy of nested array elements.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input nested array.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afliplr3d( [ [ [ 1, 2 ], [ 3, 4 ] ] ] )\n [ [ [ 2, 1 ], [ 4, 3 ] ] ]\n\n See Also\n --------\n base.afliplr2d, base.afliplr4d, base.afliplr5d\n","base.afliplr4d":"\nbase.afliplr4d( x )\n Reverses the order of elements along the last dimension of a four-\n dimensional nested input array.\n\n The function does *not* perform a deep copy of nested array elements.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input nested array.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afliplr4d( [ [ [ [ 1, 2 ], [ 3, 4 ] ] ] ] )\n [ [ [ [ 2, 1 ], [ 4, 3 ] ] ] ]\n\n See Also\n --------\n base.afliplr2d, base.afliplr3d, base.afliplr5d\n","base.afliplr5d":"\nbase.afliplr5d( x )\n Reverses the order of elements along the last dimension of a five-\n dimensional nested input array.\n\n The function does *not* perform a deep copy of nested array elements.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input nested array.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.afliplr5d( [ [ [ [ [ 1, 2 ], [ 3, 4 ] ] ] ] ] )\n [ [ [ [ [ 2, 1 ], [ 4, 3 ] ] ] ] ]\n\n See Also\n --------\n base.afliplr2d, base.afliplr3d, base.afliplr4d\n","base.aflipud2d":"\nbase.aflipud2d( x )\n Reverses the order of elements along the first dimension of a two-\n dimensional nested input array.\n\n The function does *not* perform a deep copy of nested array elements.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input nested array.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aflipud2d( [ [ 1, 2 ], [ 3, 4 ] ] )\n [ [ 3, 4 ], [ 1, 2 ] ]\n\n See Also\n --------\n base.aflipud3d, base.aflipud4d, base.aflipud5d\n","base.aflipud3d":"\nbase.aflipud3d( x )\n Reverses the order of elements along the second-to-last dimension of a\n three-dimensional nested input array.\n\n The function does *not* perform a deep copy of nested array elements.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input nested array.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aflipud3d( [ [ [ 1, 2 ], [ 3, 4 ] ] ] )\n [ [ [ 3, 4 ], [ 1, 2 ] ] ]\n\n See Also\n --------\n base.aflipud2d, base.aflipud4d, base.aflipud5d\n","base.aflipud4d":"\nbase.aflipud4d( x )\n Reverses the order of elements along the second-to-last dimension of a four-\n dimensional nested input array.\n\n The function does *not* perform a deep copy of nested array elements.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input nested array.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aflipud4d( [ [ [ [ 1, 2 ], [ 3, 4 ] ] ] ] )\n [ [ [ [ 3, 4 ], [ 1, 2 ] ] ] ]\n\n See Also\n --------\n base.aflipud2d, base.aflipud3d, base.aflipud5d\n","base.aflipud5d":"\nbase.aflipud5d( x )\n Reverses the order of elements along the second-to-last dimension of a five-\n dimensional nested input array.\n\n The function does *not* perform a deep copy of nested array elements.\n\n Parameters\n ----------\n x: ArrayLikeObject\n Input nested array.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aflipud5d( [ [ [ [ [ 1, 2 ], [ 3, 4 ] ] ] ] ] )\n [ [ [ [ [ 3, 4 ], [ 1, 2 ] ] ] ] ]\n\n See Also\n --------\n base.aflipud2d, base.aflipud3d, base.aflipud4d\n","base.ahavercos":"\nbase.ahavercos( x )\n Computes the inverse half-value versed cosine.\n\n The inverse half-value versed cosine is defined as `2*acos(sqrt(x))`.\n\n If `x < 0`, `x > 1`, or `x` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse half-value versed cosine.\n\n Examples\n --------\n > var y = base.ahavercos( 0.5 )\n ~1.5708\n > y = base.ahavercos( 0.0 )\n ~3.1416\n\n See Also\n --------\n base.ahaversin, base.havercos, base.vercos\n","base.ahaversin":"\nbase.ahaversin( x )\n Computes the inverse half-value versed sine.\n\n The inverse half-value versed sine is defined as `2*asin(sqrt(x))`.\n\n If `x < 0`, `x > 1`, or `x` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse half-value versed sine.\n\n Examples\n --------\n > var y = base.ahaversin( 0.5 )\n ~1.5708\n > y = base.ahaversin( 0.0 )\n 0.0\n\n See Also\n --------\n base.ahavercos, base.haversin, base.versin\n","base.altcase":"\nbase.altcase( str )\n Converts a string to alternate case.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Alternate-cased string.\n\n Examples\n --------\n > var out = base.altcase( 'Hello World!' )\n 'hElLo wOrLd!'\n > out = base.altcase( 'I am a tiny little teapot' )\n 'i aM A TiNy lItTlE TeApOt'\n\n See Also\n --------\n base.lowercase, base.uppercase","base.aones":"\nbase.aones( len )\n Returns a \"generic\" array filled with ones.\n\n Parameters\n ----------\n len: integer\n Array length.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aones( 3 )\n [ 1.0, 1.0, 1.0 ]\n\n See Also\n --------\n base.azeros, base.aones2d, base.aones3d, base.aones4d, base.aones5d, base.aonesnd\n","base.aones2d":"\nbase.aones2d( shape )\n Returns a two-dimensional nested array filled with ones.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aones2d( [ 1, 3 ] )\n [ [ 1.0, 1.0, 1.0 ] ]\n\n See Also\n --------\n base.azeros2d, base.aones, base.aones3d, base.aones4d, base.aones5d, base.aonesnd\n","base.aones3d":"\nbase.aones3d( shape )\n Returns a three-dimensional nested array filled with ones.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aones3d( [ 1, 1, 3 ] )\n [ [ [ 1.0, 1.0, 1.0 ] ] ]\n\n See Also\n --------\n base.azeros3d, base.aones, base.aones2d, base.aones4d, base.aones5d, base.aonesnd\n","base.aones4d":"\nbase.aones4d( shape )\n Returns a four-dimensional nested array filled with ones.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aones4d( [ 1, 1, 1, 3 ] )\n [ [ [ [ 1.0, 1.0, 1.0 ] ] ] ]\n\n See Also\n --------\n base.azeros4d, base.aones, base.aones2d, base.aones3d, base.aones5d, base.aonesnd\n","base.aones5d":"\nbase.aones5d( shape )\n Returns a five-dimensional nested array filled with ones.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aones5d( [ 1, 1, 1, 1, 3 ] )\n [ [ [ [ [ 1.0, 1.0, 1.0 ] ] ] ] ]\n\n See Also\n --------\n base.azeros5d, base.aones, base.aones2d, base.aones3d, base.aones4d, base.aonesnd\n","base.aonesnd":"\nbase.aonesnd( shape )\n Returns an n-dimensional nested array filled with ones.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.aonesnd( [ 1, 3 ] )\n [ [ 1.0, 1.0, 1.0 ] ]\n\n See Also\n --------\n base.azerosnd, base.aones, base.aones2d, base.aones3d, base.aones4d, base.aones5d\n","base.aoneTo":"\nbase.aoneTo( n )\n Generates a linearly spaced numeric array whose elements increment by 1\n starting from one.\n\n If `n` is a non-integer value greater than zero, the function returns an\n array having `ceil(n)` elements.\n\n If `n` is less than or equal to zero, the function returns an empty array.\n\n Parameters\n ----------\n n: number\n Number of elements.\n\n Returns\n -------\n out: Array\n Linearly spaced numeric array.\n\n Examples\n --------\n > var arr = base.aoneTo( 6 )\n [ 1, 2, 3, 4, 5, 6 ]\n\n\nbase.aoneTo.assign( out, stride, offset )\n Fills an array with linearly spaced numeric elements which increment by 1\n starting from one.\n\n Parameters\n ----------\n out: ArrayLikeObject\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: ArrayLikeObject\n Output array.\n\n Examples\n --------\n > var out = [ 0, 0, 0, 0, 0, 0 ];\n > base.aoneTo.assign( out, -1, out.length-1 );\n > out\n [ 6, 5, 4, 3, 2, 1 ]\n\n See Also\n --------\n base.azeroTo, base.aones\n","base.aoneTo.assign":"\nbase.aoneTo.assign( out, stride, offset )\n Fills an array with linearly spaced numeric elements which increment by 1\n starting from one.\n\n Parameters\n ----------\n out: ArrayLikeObject\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: ArrayLikeObject\n Output array.\n\n Examples\n --------\n > var out = [ 0, 0, 0, 0, 0, 0 ];\n > base.aoneTo.assign( out, -1, out.length-1 );\n > out\n [ 6, 5, 4, 3, 2, 1 ]\n\n See Also\n --------\n base.azeroTo, base.aones","base.args2multislice":"\nbase.args2multislice( args )\n Creates a MultiSlice object from a list of MultiSlice constructor arguments.\n\n Parameters\n ----------\n args: Array\n Constructor arguments.\n\n Returns\n -------\n s: MultiSlice\n MultiSlice instance.\n\n Examples\n --------\n > var args = [ null, null, null ];\n > var s = new base.args2multislice( args );\n > s.data\n [ null, null, null ]\n > args = [ 10, new Slice( 0, 10, 1 ), null ];\n > s = new base.args2multislice( args );\n > s.data\n [ 10, , null ]\n\n","base.asec":"\nbase.asec( x )\n Computes the inverse (arc) secant of a number.\n\n If `x > -1` and `x < 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse (arc) secant.\n\n Examples\n --------\n > var y = base.asec( 1.0 )\n 0.0\n > y = base.asec( 2.0 )\n ~1.0472\n > y = base.asec( NaN )\n NaN\n\n See Also\n --------\n base.acot, base.acsc, base.asech, base.acos\n","base.asecd":"\nbase.asecd( x )\n Computes the arcsecant (in degrees) of a double-precision floating-point\n number.\n\n If `x` does not satisy `x >= 1` or `x <= -1`, the function returns NaN.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arcsecant (in degrees).\n\n Examples\n --------\n > var y = base.asecd( 0.0 )\n NaN\n > y = base.asecd( 2 )\n ~60.0\n > y = base.asecd( NaN )\n NaN\n\n See Also\n --------\n base.asec, base.asech, base.acosd, base.secd\n","base.asecdf":"\nbase.asecdf( x )\n Computes the arcsecant (in degrees) of a single-precision floating-point\n number.\n\n If `x` does not satisy `x >= 1` or `x <= -1`, the function returns NaN.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arcsecant (in degrees).\n\n Examples\n --------\n > var y = base.asecdf( 2.0 )\n ~60.0\n > y = base.asecdf( 0.0 )\n NaN\n > y = base.asecdf( NaN )\n NaN\n\n See Also\n --------\n base.asec, base.asech\n","base.asecf":"\nbase.asecf( x )\n Computes the inverse (arc) secant of a single-precision\n floating-point number.\n\n If `x > -1` and `x < 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse (arc) secant.\n\n Examples\n --------\n > var y = base.asecf( 1.0 )\n 0.0\n > y = base.asecf( 2.0 )\n ~1.0472\n > y = base.asecf( NaN )\n NaN\n\n See Also\n --------\n base.asec, base.asech, base.acosf\n","base.asech":"\nbase.asech( x )\n Computes the hyperbolic arcsecant of a number.\n\n If `x < 0` or `x > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Hyperbolic arcsecant.\n\n Examples\n --------\n > var y = base.asech( 1.0 )\n 0.0\n > y = base.asech( 0.5 )\n ~1.317\n > y = base.asech( NaN )\n NaN\n\n See Also\n --------\n base.acosh, base.asec, base.asech, base.acoth\n","base.asin":"\nbase.asin( x )\n Computes the arcsine of a double-precision floating-point number.\n\n If `|x| > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arcsine (in radians).\n\n Examples\n --------\n > var y = base.asin( 0.0 )\n 0.0\n > y = base.asin( -PI/6.0 )\n ~-0.551\n > y = base.asin( NaN )\n NaN\n\n See Also\n --------\n base.acos, base.asinh, base.atan\n","base.asind":"\nbase.asind( x )\n Computes the arcsine (in degrees) of a double-precision floating-point\n number.\n\n If `|x| > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arcsine (in degrees).\n\n Examples\n --------\n > var y = base.asind( 0.0 )\n 0.0\n > y = base.asind( PI / 6.0 )\n ~31.57\n > y = base.asind( NaN )\n NaN\n\n See Also\n --------\n base.asin, base.asinh, base.atand\n","base.asindf":"\nbase.asindf( x )\n Computes the arcsine (in degrees) of a single-precision floating-point\n number.\n\n If `|x| > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arcsine (in degrees).\n\n Examples\n --------\n > var y = base.asindf( 0.0 )\n 0.0\n > y = base.asindf( 3.1415927410125732 / 6.0 )\n ~31.57\n > y = base.asindf( NaN )\n NaN\n\n See Also\n --------\n base.asinf, base.asind\n","base.asinf":"\nbase.asinf( x )\n Computes the arcsine of a single-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arcsine (in radians).\n\n Examples\n --------\n > var y = base.asinf( 0.0 )\n 0.0\n > y = base.asinf( -3.14/6.0 )\n ~-0.551\n > y = base.asinf( NaN )\n NaN\n\n See Also\n --------\n base.asin, base.asindf\n","base.asinh":"\nbase.asinh( x )\n Computes the hyperbolic arcsine of a double-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Hyperbolic arcsine.\n\n Examples\n --------\n > var y = base.asinh( 0.0 )\n 0.0\n > y = base.asinh( 2.0 )\n ~1.444\n > y = base.asinh( -2.0 )\n ~-1.444\n > y = base.asinh( NaN )\n NaN\n > y = base.asinh( NINF )\n -Infinity\n > y = base.asinh( PINF )\n Infinity\n\n See Also\n --------\n base.acosh, base.asin, base.atanh\n","base.atan":"\nbase.atan( x )\n Computes the arctangent of a double-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arctangent (in radians).\n\n Examples\n --------\n > var y = base.atan( 0.0 )\n ~0.0\n > y = base.atan( -PI/2.0 )\n ~-1.004\n > y = base.atan( PI/2.0 )\n ~1.004\n > y = base.atan( NaN )\n NaN\n\n See Also\n --------\n base.acos, base.asin, base.atanh\n","base.atan2":"\nbase.atan2( y, x )\n Computes the angle in the plane (in radians) between the positive x-axis and\n the ray from (0,0) to the point (x,y).\n\n Parameters\n ----------\n y: number\n Coordinate along y-axis.\n\n x: number\n Coordinate along x-axis.\n\n Returns\n -------\n out: number\n Angle (in radians).\n\n Examples\n --------\n > var v = base.atan2( 2.0, 2.0 )\n ~0.785\n > v = base.atan2( 6.0, 2.0 )\n ~1.249\n > v = base.atan2( -1.0, -1.0 )\n ~-2.356\n > v = base.atan2( 3.0, 0.0 )\n ~1.571\n > v = base.atan2( -2.0, 0.0 )\n ~-1.571\n > v = base.atan2( 0.0, 0.0 )\n 0.0\n > v = base.atan2( 3.0, NaN )\n NaN\n > v = base.atan2( NaN, 2.0 )\n NaN\n\n See Also\n --------\n base.atan\n","base.atand":"\nbase.atand( x )\n Computes the arctangent (in degrees) of a double-precision floating-point\n number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arctangent (in degrees).\n\n Examples\n --------\n > var y = base.atand( 0.0 )\n 0.0\n > y = base.atand( PI/6.0 )\n ~27.64\n > y = base.atand( NaN )\n NaN\n\n See Also\n --------\n base.atan, base.atanh, base.acosd\n","base.atanf":"\nbase.atanf( x )\n Computes the arctangent of a single-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Arctangent (in radians).\n\n Examples\n --------\n > var y = base.atanf( 0.0 )\n 0.0\n > y = base.atanf( -3.14/4.0 )\n ~-0.666\n > y = base.atanf( 3.14/4.0 )\n ~0.666\n > y = base.atanf( NaN )\n NaN\n\n See Also\n --------\n base.atan, base.atanh, base.acosf\n","base.atanh":"\nbase.atanh( x )\n Computes the hyperbolic arctangent of a double-precision floating-point\n number.\n\n If `|x| > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Hyperbolic arctangent.\n\n Examples\n --------\n > var y = base.atanh( 0.0 )\n 0.0\n > y = base.atanh( 0.9 )\n ~1.472\n > y = base.atanh( 1.0 )\n Infinity\n > y = base.atanh( -1.0 )\n -Infinity\n > y = base.atanh( NaN )\n NaN\n\n See Also\n --------\n base.acosh, base.asinh, base.atan\n","base.avercos":"\nbase.avercos( x )\n Computes the inverse versed cosine.\n\n The inverse versed cosine is defined as `acos(1+x)`.\n\n If `x < -2`, `x > 0`, or `x` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse versed cosine.\n\n Examples\n --------\n > var y = base.avercos( -1.5 )\n ~2.0944\n > y = base.avercos( -0.0 )\n 0.0\n\n See Also\n --------\n base.aversin, base.versin\n","base.aversin":"\nbase.aversin( x )\n Computes the inverse versed sine.\n\n The inverse versed sine is defined as `acos(1-x)`.\n\n If `x < 0`, `x > 2`, or `x` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Inverse versed sine.\n\n Examples\n --------\n > var y = base.aversin( 1.5 )\n ~2.0944\n > y = base.aversin( 0.0 )\n 0.0\n\n See Also\n --------\n base.avercos, base.vercos\n","base.azeros":"\nbase.azeros( len )\n Returns a zero-filled \"generic\" array.\n\n Parameters\n ----------\n len: integer\n Array length.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.azeros( 3 )\n [ 0.0, 0.0, 0.0 ]\n\n See Also\n --------\n base.aones, base.azeros2d, base.azeros3d, base.azeros4d, base.azeros5d, base.azerosnd\n","base.azeros2d":"\nbase.azeros2d( shape )\n Returns a zero-filled two-dimensional nested array.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.azeros2d( [ 1, 3 ] )\n [ [ 0.0, 0.0, 0.0 ] ]\n\n See Also\n --------\n base.azeros, base.aones2d, base.azeros3d, base.azeros4d, base.azeros5d, base.azerosnd\n","base.azeros3d":"\nbase.azeros3d( shape )\n Returns a zero-filled three-dimensional nested array.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.azeros3d( [ 1, 1, 3 ] )\n [ [ [ 0.0, 0.0, 0.0 ] ] ]\n\n See Also\n --------\n base.azeros, base.aones3d, base.azeros2d, base.azeros4d, base.azeros5d, base.azerosnd\n","base.azeros4d":"\nbase.azeros4d( shape )\n Returns a zero-filled four-dimensional nested array.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.azeros4d( [ 1, 1, 1, 3 ] )\n [ [ [ [ 0.0, 0.0, 0.0 ] ] ] ]\n\n See Also\n --------\n base.azeros, base.aones4d, base.azeros2d, base.azeros3d, base.azeros5d, base.azerosnd\n","base.azeros5d":"\nbase.azeros5d( shape )\n Returns a zero-filled five-dimensional nested array.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.azeros5d( [ 1, 1, 1, 1, 3 ] )\n [ [ [ [ [ 0.0, 0.0, 0.0 ] ] ] ] ]\n\n See Also\n --------\n base.azeros, base.aones5d, base.azeros2d, base.azeros3d, base.azeros4d, base.azerosnd\n","base.azerosnd":"\nbase.azerosnd( shape )\n Returns a zero-filled n-dimensional nested array.\n\n Parameters\n ----------\n shape: Array\n Array shape.\n\n Returns\n -------\n out: Array\n Output array.\n\n Examples\n --------\n > var out = base.azerosnd( [ 1, 3 ] )\n [ [ 0.0, 0.0, 0.0 ] ]\n\n See Also\n --------\n base.azeros, base.aonesnd, base.azeros2d, base.azeros3d, base.azeros4d, base.azeros5d\n","base.azeroTo":"\nbase.azeroTo( n )\n Generates a linearly spaced numeric array whose elements increment by 1\n starting from zero.\n\n If `n` is a non-integer value greater than zero, the function returns an\n array having `ceil(n)` elements.\n\n If `n` is less than or equal to zero, the function returns an empty array.\n\n Parameters\n ----------\n n: number\n Number of elements.\n\n Returns\n -------\n out: Array\n Linearly spaced numeric array.\n\n Examples\n --------\n > var arr = base.azeroTo( 6 )\n [ 0, 1, 2, 3, 4, 5 ]\n\n\nbase.azeroTo.assign( out, stride, offset )\n Fills an array with linearly spaced numeric elements which increment by 1\n starting from zero.\n\n Parameters\n ----------\n out: ArrayLikeObject\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: ArrayLikeObject\n Output array.\n\n Examples\n --------\n > var out = [ 0, 0, 0, 0, 0, 0 ];\n > base.azeroTo.assign( out, -1, out.length-1 );\n > out\n [ 5, 4, 3, 2, 1, 0 ]\n\n See Also\n --------\n base.aoneTo\n","base.azeroTo.assign":"\nbase.azeroTo.assign( out, stride, offset )\n Fills an array with linearly spaced numeric elements which increment by 1\n starting from zero.\n\n Parameters\n ----------\n out: ArrayLikeObject\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: ArrayLikeObject\n Output array.\n\n Examples\n --------\n > var out = [ 0, 0, 0, 0, 0, 0 ];\n > base.azeroTo.assign( out, -1, out.length-1 );\n > out\n [ 5, 4, 3, 2, 1, 0 ]\n\n See Also\n --------\n base.aoneTo","base.bernoulli":"\nbase.bernoulli( n )\n Computes the nth Bernoulli number.\n\n If not provided a nonnegative integer value, the function returns `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Input value.\n\n Returns\n -------\n y: number\n Bernoulli number.\n\n Examples\n --------\n > var y = base.bernoulli( 0 )\n 1.0\n > y = base.bernoulli( 1 )\n 0.0\n > y = base.bernoulli( 2 )\n ~0.167\n > y = base.bernoulli( 3 )\n 0.0\n > y = base.bernoulli( 4 )\n ~-0.033\n > y = base.bernoulli( 5 )\n 0.0\n > y = base.bernoulli( 20 )\n ~-529.124\n > y = base.bernoulli( 260 )\n -Infinity\n > y = base.bernoulli( 262 )\n Infinity\n > y = base.bernoulli( NaN )\n NaN\n\n","base.besselj0":"\nbase.besselj0( x )\n Computes the Bessel function of the first kind of order zero.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.besselj0( 0.0 )\n 1.0\n > y = base.besselj0( 1.0 )\n ~0.765\n > y = base.besselj0( PINF )\n 0.0\n > y = base.besselj0( NINF )\n 0.0\n > y = base.besselj0( NaN )\n NaN\n\n See Also\n --------\n base.besselj1, base.bessely0, base.bessely1\n","base.besselj1":"\nbase.besselj1( x )\n Computes the Bessel function of the first kind of order one.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.besselj1( 0.0 )\n 0.0\n > y = base.besselj1( 1.0 )\n ~0.440\n > y = base.besselj1( PINF )\n 0.0\n > y = base.besselj1( NINF )\n 0.0\n > y = base.besselj1( NaN )\n NaN\n\n See Also\n --------\n base.besselj0, base.bessely0, base.bessely1\n","base.bessely0":"\nbase.bessely0( x )\n Computes the Bessel function of the second kind of order zero.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.bessely0( 0.0 )\n -Infinity\n > y = base.bessely0( 1.0 )\n ~0.088\n > y = base.bessely0( -1.0 )\n NaN\n > y = base.bessely0( PINF )\n 0.0\n > y = base.bessely0( NINF )\n NaN\n > y = base.bessely0( NaN )\n NaN\n\n See Also\n --------\n base.besselj0, base.besselj1, base.bessely1\n","base.bessely1":"\nbase.bessely1( x )\n Computes the Bessel function of the second kind of order one.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.bessely1( 0.0 )\n -Infinity\n > y = base.bessely1( 1.0 )\n ~-0.781\n > y = base.bessely1( -1.0 )\n NaN\n > y = base.bessely1( PINF )\n 0.0\n > y = base.bessely1( NINF )\n NaN\n > y = base.bessely1( NaN )\n NaN\n\n See Also\n --------\n base.besselj0, base.besselj1, base.bessely0\n","base.beta":"\nbase.beta( x, y )\n Evaluates the beta function.\n\n Parameters\n ----------\n x: number\n First function parameter (nonnegative).\n\n y: number\n Second function parameter (nonnegative).\n\n Returns\n -------\n out: number\n Evaluated beta function.\n\n Examples\n --------\n > var v = base.beta( 0.0, 0.5 )\n Infinity\n > v = base.beta( 1.0, 1.0 )\n 1.0\n > v = base.beta( -1.0, 2.0 )\n NaN\n > v = base.beta( 5.0, 0.2 )\n ~3.382\n > v = base.beta( 4.0, 1.0 )\n 0.25\n > v = base.beta( NaN, 2.0 )\n NaN\n\n See Also\n --------\n base.betainc, base.betaincinv, base.betaln\n","base.betainc":"\nbase.betainc( x, a, b[, regularized[, upper]] )\n Computes the regularized incomplete beta function.\n\n The `regularized` and `upper` parameters specify whether to evaluate the\n non-regularized and/or upper incomplete beta functions, respectively.\n\n If provided `x < 0` or `x > 1`, the function returns `NaN`.\n\n If provided `a < 0` or `b < 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n First function parameter.\n\n a: number\n Second function parameter.\n\n b: number\n Third function parameter.\n\n regularized: boolean (optional)\n Boolean indicating whether the function should evaluate the regularized\n or non-regularized incomplete beta function. Default: `true`.\n\n upper: boolean (optional)\n Boolean indicating whether the function should return the upper tail of\n the incomplete beta function. Default: `false`.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.betainc( 0.5, 2.0, 2.0 )\n 0.5\n > y = base.betainc( 0.5, 2.0, 2.0, false )\n ~0.083\n > y = base.betainc( 0.2, 1.0, 2.0 )\n 0.36\n > y = base.betainc( 0.2, 1.0, 2.0, true, true )\n 0.64\n > y = base.betainc( NaN, 1.0, 1.0 )\n NaN\n > y = base.betainc( 0.8, NaN, 1.0 )\n NaN\n > y = base.betainc( 0.8, 1.0, NaN )\n NaN\n > y = base.betainc( 1.5, 1.0, 1.0 )\n NaN\n > y = base.betainc( -0.5, 1.0, 1.0 )\n NaN\n > y = base.betainc( 0.5, -2.0, 2.0 )\n NaN\n > y = base.betainc( 0.5, 2.0, -2.0 )\n NaN\n\n See Also\n --------\n base.beta, base.betaincinv, base.betaln\n","base.betaincinv":"\nbase.betaincinv( p, a, b[, upper] )\n Computes the inverse of the lower incomplete beta function.\n\n In contrast to a more commonly used definition, the first argument is the\n probability `p` and the second and third arguments are `a` and `b`,\n respectively.\n\n By default, the function inverts the lower regularized incomplete beta\n function. To invert the upper function, set the `upper` argument to `true`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Probability.\n\n a: number\n Second function parameter.\n\n b: number\n Third function parameter.\n\n upper: boolean (optional)\n Boolean indicating if the function should invert the upper tail of the\n incomplete beta function. Default: `false`.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.betaincinv( 0.2, 3.0, 3.0 )\n ~0.327\n > y = base.betaincinv( 0.4, 3.0, 3.0 )\n ~0.446\n > y = base.betaincinv( 0.4, 3.0, 3.0, true )\n ~0.554\n > y = base.betaincinv( 0.4, 1.0, 6.0 )\n ~0.082\n > y = base.betaincinv( 0.8, 1.0, 6.0 )\n ~0.235\n > y = base.betaincinv( NaN, 1.0, 1.0 )\n NaN\n > y = base.betaincinv( 0.5, NaN, 1.0 )\n NaN\n > y = base.betaincinv( 0.5, 1.0, NaN )\n NaN\n > y = base.betaincinv( 1.2, 1.0, 1.0 )\n NaN\n > y = base.betaincinv( -0.5, 1.0, 1.0 )\n NaN\n > y = base.betaincinv( 0.5, -2.0, 2.0 )\n NaN\n > y = base.betaincinv( 0.5, 0.0, 2.0 )\n NaN\n > y = base.betaincinv( 0.5, 2.0, -2.0 )\n NaN\n > y = base.betaincinv( 0.5, 2.0, 0.0 )\n NaN\n\n See Also\n --------\n base.beta, base.betainc, base.betaln\n","base.betaln":"\nbase.betaln( a, b )\n Evaluates the natural logarithm of the beta function.\n\n Parameters\n ----------\n a: number\n First function parameter (nonnegative).\n\n b: number\n Second function parameter (nonnegative).\n\n Returns\n -------\n out: number\n Natural logarithm of the beta function.\n\n Examples\n --------\n > var v = base.betaln( 0.0, 0.0 )\n Infinity\n > v = base.betaln( 1.0, 1.0 )\n 0.0\n > v = base.betaln( -1.0, 2.0 )\n NaN\n > v = base.betaln( 5.0, 0.2 )\n ~1.218\n > v = base.betaln( 4.0, 1.0 )\n ~-1.386\n > v = base.betaln( NaN, 2.0 )\n NaN\n\n See Also\n --------\n base.beta, base.betainc, base.betaincinv\n","base.binet":"\nbase.binet( x )\n Evaluates Binet's formula extended to real numbers.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function result.\n\n Examples\n --------\n > var y = base.binet( 0.0 )\n 0.0\n > y = base.binet( 1.0 )\n 1.0\n > y = base.binet( 2.0 )\n 1.0\n > y = base.binet( 3.0 )\n 2.0\n > y = base.binet( 4.0 )\n 3.0\n > y = base.binet( 5.0 )\n ~5.0\n > y = base.binet( NaN )\n NaN\n\n See Also\n --------\n base.fibonacci, base.negafibonacci\n","base.binomcoef":"\nbase.binomcoef( n, k )\n Computes the binomial coefficient of two integers.\n\n If `k < 0`, the function returns `0`.\n\n The function returns `NaN` for non-integer `n` or `k`.\n\n Parameters\n ----------\n n: integer\n First input value.\n\n k: integer\n Second input value.\n\n Returns\n -------\n out: number\n Function value.\n\n Examples\n --------\n > var v = base.binomcoef( 8, 2 )\n 28\n > v = base.binomcoef( 0, 0 )\n 1\n > v = base.binomcoef( -4, 2 )\n 10\n > v = base.binomcoef( 5, 3 )\n 10\n > v = base.binomcoef( NaN, 3 )\n NaN\n > v = base.binomcoef( 5, NaN )\n NaN\n > v = base.binomcoef( NaN, NaN )\n NaN\n\n","base.binomcoefln":"\nbase.binomcoefln( n, k )\n Computes the natural logarithm of the binomial coefficient of two integers.\n\n If `k < 0`, the function returns negative infinity.\n\n The function returns `NaN` for non-integer `n` or `k`.\n\n Parameters\n ----------\n n: integer\n First input value.\n\n k: integer\n Second input value.\n\n Returns\n -------\n out: number\n Natural logarithm of the binomial coefficient.\n\n Examples\n --------\n > var v = base.binomcoefln( 8, 2 )\n ~3.332\n > v = base.binomcoefln( 0, 0 )\n 0.0\n > v = base.binomcoefln( -4, 2 )\n ~2.303\n > v = base.binomcoefln( 88, 3 )\n ~11.606\n > v = base.binomcoefln( NaN, 3 )\n NaN\n > v = base.binomcoefln( 5, NaN )\n NaN\n > v = base.binomcoefln( NaN, NaN )\n NaN\n\n","base.boxcox":"\nbase.boxcox( x, lambda )\n Computes a one-parameter Box-Cox transformation.\n\n Parameters\n ----------\n x: number\n Input value.\n\n lambda: number\n Power parameter.\n\n Returns\n -------\n b: number\n Function value.\n\n Examples\n --------\n > var v = base.boxcox( 1.0, 2.5 )\n 0.0\n > v = base.boxcox( 4.0, 2.5 )\n 12.4\n > v = base.boxcox( 10.0, 2.5 )\n ~126.0911\n > v = base.boxcox( 2.0, 0.0 )\n ~0.6931\n > v = base.boxcox( -1.0, 2.5 )\n NaN\n > v = base.boxcox( 0.0, -1.0 )\n -Infinity\n\n See Also\n --------\n base.boxcoxinv, base.boxcox1p, base.boxcox1pinv","base.boxcox1p":"\nbase.boxcox1p( x, lambda )\n Computes a one-parameter Box-Cox transformation of 1+x.\n\n Parameters\n ----------\n x: number\n Input value.\n\n lambda: number\n Power parameter.\n\n Returns\n -------\n b: number\n Function value.\n\n Examples\n --------\n > var v = base.boxcox1p( 1.0, 2.5 )\n ~1.8627\n > v = base.boxcox1p( 4.0, 2.5 )\n ~21.9607\n > v = base.boxcox1p( 10.0, 2.5 )\n ~160.1246\n > v = base.boxcox1p( 2.0, 0.0 )\n ~1.0986\n > v = base.boxcox1p( -1.0, 2.5 )\n -0.4\n > v = base.boxcox1p( 0.0, -1.0 )\n 0.0\n > v = base.boxcox1p( -1.0, -1.0 )\n -Infinity\n\n See Also\n --------\n base.boxcox, base.boxcox1pinv, base.boxcoxinv","base.boxcox1pinv":"\nbase.boxcox1pinv( y, lambda )\n Computes the inverse of a one-parameter Box-Cox transformation for 1+x.\n\n Parameters\n ----------\n y: number\n Input value.\n\n lambda: number\n Power parameter.\n\n Returns\n -------\n v: number\n Function value.\n\n Examples\n --------\n > var v = base.boxcox1pinv( 1.0, 2.5 )\n ~0.6505\n > v = base.boxcox1pinv( 4.0, 2.5 )\n ~1.6095\n > v = base.boxcox1pinv( 10.0, 2.5 )\n ~2.6812\n > v = base.boxcox1pinv( 2.0, 0.0 )\n ~6.3891\n > v = base.boxcox1pinv( -1.0, 2.5 )\n NaN\n > v = base.boxcox1pinv( 0.0, -1.0 )\n 0.0\n > v = base.boxcox1pinv( 1.0, NaN )\n NaN\n > v = base.boxcox1pinv( NaN, 3.1 )\n NaN\n\n See Also\n --------\n base.boxcox, base.boxcox1p, base.boxcoxinv","base.boxcoxinv":"\nbase.boxcoxinv( y, lambda )\n Computes the inverse of a one-parameter Box-Cox transformation.\n\n Parameters\n ----------\n y: number\n Input value.\n\n lambda: number\n Power parameter.\n\n Returns\n -------\n b: number\n Function value.\n\n Examples\n --------\n > var v = base.boxcoxinv( 1.0, 2.5 )\n ~1.6505\n > v = base.boxcoxinv( 4.0, 2.5 )\n ~2.6095\n > v = base.boxcoxinv( 10.0, 2.5 )\n ~3.6812\n > v = base.boxcoxinv( 2.0, 0.0 )\n ~7.3891\n > v = base.boxcoxinv( -1.0, 2.5 )\n NaN\n > v = base.boxcoxinv( 0.0, -1.0 )\n 1.0\n > v = base.boxcoxinv( 1.0, NaN )\n NaN\n > v = base.boxcoxinv( NaN, 3.1 )\n NaN\n\n See Also\n --------\n base.boxcox, base.boxcox1p, base.boxcox1pinv","base.cabs":"\nbase.cabs( z )\n Computes the absolute value of a double-precision complex floating-point\n number.\n\n The absolute value of a complex number is its distance from zero.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n y: number\n Absolute value.\n\n Examples\n --------\n > var y = base.cabs( new Complex128( 5.0, 3.0 ) )\n ~5.831\n\n See Also\n --------\n base.cabs2, base.abs\n","base.cabs2":"\nbase.cabs2( z )\n Computes the squared absolute value of a double-precision complex floating-\n point number.\n\n The absolute value of a complex number is its distance from zero.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n y: number\n Squared absolute value.\n\n Examples\n --------\n > var y = base.cabs2( new Complex128( 5.0, 3.0 ) )\n 34.0\n\n See Also\n --------\n base.cabs, base.abs2\n","base.cabs2f":"\nbase.cabs2f( z )\n Computes the squared absolute value of a single-precision complex floating-\n point number.\n\n The absolute value of a complex number is its distance from zero.\n\n Parameters\n ----------\n z: Complex64\n Complex number.\n\n Returns\n -------\n y: number\n Squared absolute value.\n\n Examples\n --------\n > var y = base.cabs2f( new Complex64( 5.0, 3.0 ) )\n 34.0\n\n See Also\n --------\n base.cabs2, base.cabsf, base.abs2f\n","base.cabsf":"\nbase.cabsf( z )\n Computes the absolute value of a single-precision complex floating-point\n number.\n\n The absolute value of a complex number is its distance from zero.\n\n Parameters\n ----------\n z: Complex64\n Complex number.\n\n Returns\n -------\n y: number\n Absolute value.\n\n Examples\n --------\n > var y = base.cabsf( new Complex64( 5.0, 3.0 ) )\n ~5.831\n\n See Also\n --------\n base.cabs, base.cabs2f, base.absf\n","base.cadd":"\nbase.cadd( z1, z2 )\n Adds two double-precision complex floating-point numbers.\n\n Parameters\n ----------\n z1: Complex128\n Complex number.\n\n z2: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var z = new Complex128( 5.0, 3.0 )\n \n > var out = base.cadd( z, z )\n \n > var re = real( out )\n 10.0\n > var im = imag( out )\n 6.0\n\n See Also\n --------\n base.cdiv, base.cmul, base.csub\n","base.caddf":"\nbase.caddf( z1, z2 )\n Adds two single-precision complex floating-point numbers.\n\n Parameters\n ----------\n z1: Complex64\n Complex number.\n\n z2: Complex64\n Complex number.\n\n Returns\n -------\n out: Complex64\n Result.\n\n Examples\n --------\n > var z = new Complex64( 5.0, 3.0 )\n \n > var out = base.caddf( z, z )\n \n > var re = realf( out )\n 10.0\n > var im = imagf( out )\n 6.0\n\n See Also\n --------\n base.cadd, base.cmulf, base.csubf\n","base.camelcase":"\nbase.camelcase( str )\n Converts a string to camel case.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Camel-cased string.\n\n Examples\n --------\n > var out = base.camelcase( 'Hello World!' )\n 'helloWorld'\n > out = base.camelcase( 'beep boop' )\n 'beepBoop'\n\n See Also\n --------\n base.constantcase, base.lowercase, base.snakecase, base.uppercase","base.capitalize":"\nbase.capitalize( str )\n Capitalizes the first character in a string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Capitalized string.\n\n Examples\n --------\n > var out = base.capitalize( 'beep' )\n 'Beep'\n > out = base.capitalize( 'Boop' )\n 'Boop'\n\n See Also\n --------\n base.lowercase, base.uppercase\n","base.cbrt":"\nbase.cbrt( x )\n Computes the cube root of a double-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Cube root.\n\n Examples\n --------\n > var y = base.cbrt( 64.0 )\n 4.0\n > y = base.cbrt( 27.0 )\n 3.0\n > y = base.cbrt( 0.0 )\n 0.0\n > y = base.cbrt( -0.0 )\n -0.0\n > y = base.cbrt( -9.0 )\n ~-2.08\n > y = base.cbrt( NaN )\n NaN\n\n See Also\n --------\n base.pow, base.sqrt\n","base.cbrtf":"\nbase.cbrtf( x )\n Computes the cube root of a single-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Cube root.\n\n Examples\n --------\n > var y = base.cbrtf( 64.0 )\n 4.0\n > y = base.cbrtf( 27.0 )\n 3.0\n > y = base.cbrtf( 0.0 )\n 0.0\n > y = base.cbrtf( -0.0 )\n -0.0\n > y = base.cbrtf( -9.0 )\n ~-2.08\n > y = base.cbrtf( NaN )\n NaN\n\n See Also\n --------\n base.cbrt, base.sqrtf\n","base.cceil":"\nbase.cceil( z )\n Rounds a double-precision complex floating-point number toward positive\n infinity.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var v = base.cceil( new Complex128( -1.5, 2.5 ) )\n \n > var re = real( v )\n -1.0\n > var im = imag( v )\n 3.0\n\n See Also\n --------\n base.cceiln, base.cfloor, base.cround\n","base.cceilf":"\nbase.cceilf( z )\n Rounds a single-precision complex floating-point number toward positive\n infinity.\n\n Parameters\n ----------\n z: Complex64\n Complex number.\n\n Returns\n -------\n out: Complex64\n Result.\n\n Examples\n --------\n > var v = base.cceilf( new Complex64( -1.5, 2.5 ) )\n \n > var re = realf( v )\n -1.0\n > var im = imagf( v )\n 3.0\n\n See Also\n --------\n base.cceil\n","base.cceiln":"\nbase.cceiln( z, n )\n Rounds each component of a double-precision complex number to the nearest\n multiple of `10^n` toward positive infinity.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n n: integer\n Integer power of 10.\n\n Returns\n -------\n out: Complex128\n Real and imaginary components.\n\n Examples\n --------\n > var out = base.cceiln( new Complex128( 5.555, -3.333 ), -2 )\n \n > var re = real( out )\n 5.56\n > var im = imag( out )\n -3.33\n\n See Also\n --------\n base.cceil, base.cfloorn, base.croundn\n","base.ccis":"\nbase.ccis( z )\n Evaluates the cis function for a double-precision complex floating-point\n number.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Complex number.\n\n Examples\n --------\n > var y = base.ccis( new Complex128( 0.0, 0.0 ) )\n \n > var re = real( y )\n 1.0\n > var im = imag( y )\n 0.0\n > y = base.ccis( new Complex128( 1.0, 0.0 ) )\n \n > re = real( y )\n ~0.540\n > im = imag( y )\n ~0.841\n\n","base.cdiv":"\nbase.cdiv( z1, z2 )\n Divides two double-precision complex floating-point numbers.\n\n Parameters\n ----------\n z1: Complex128\n Complex number.\n\n z2: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var z1 = new Complex128( -13.0, -1.0 )\n \n > var z2 = new Complex128( -2.0, 1.0 )\n \n > var y = base.cdiv( z1, z2 )\n \n > var re = real( y )\n 5.0\n > var im = imag( y )\n 3.0\n\n See Also\n --------\n base.cadd, base.cmul, base.csub\n","base.ceil":"\nbase.ceil( x )\n Rounds a double-precision floating-point number toward positive infinity.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.ceil( 3.14 )\n 4.0\n > y = base.ceil( -4.2 )\n -4.0\n > y = base.ceil( -4.6 )\n -4.0\n > y = base.ceil( 9.5 )\n 10.0\n > y = base.ceil( -0.0 )\n -0.0\n\n See Also\n --------\n base.ceiln, base.floor, base.round\n","base.ceil2":"\nbase.ceil2( x )\n Rounds a numeric value to the nearest power of two toward positive infinity.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.ceil2( 3.14 )\n 4.0\n > y = base.ceil2( -4.2 )\n -4.0\n > y = base.ceil2( -4.6 )\n -4.0\n > y = base.ceil2( 9.5 )\n 16.0\n > y = base.ceil2( 13.0 )\n 16.0\n > y = base.ceil2( -13.0 )\n -8.0\n > y = base.ceil2( -0.0 )\n -0.0\n\n See Also\n --------\n base.ceil, base.ceil10, base.floor2, base.round2\n","base.ceil10":"\nbase.ceil10( x )\n Rounds a numeric value to the nearest power of ten toward positive infinity.\n\n The function may not return accurate results for subnormals due to a general\n loss in precision.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.ceil10( 3.14 )\n 10.0\n > y = base.ceil10( -4.2 )\n -1.0\n > y = base.ceil10( -4.6 )\n -1.0\n > y = base.ceil10( 9.5 )\n 10.0\n > y = base.ceil10( 13.0 )\n 100.0\n > y = base.ceil10( -13.0 )\n -10.0\n > y = base.ceil10( -0.0 )\n -0.0\n\n See Also\n --------\n base.ceil, base.ceil2, base.floor10, base.round10\n","base.ceilb":"\nbase.ceilb( x, n, b )\n Rounds a numeric value to the nearest multiple of `b^n` toward positive\n infinity.\n\n Due to floating-point rounding error, rounding may not be exact.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Integer power.\n\n b: integer\n Base.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n // Round to 4 decimal places:\n > var y = base.ceilb( 3.14159, -4, 10 )\n 3.1416\n\n // If `n = 0` or `b = 1`, standard round behavior:\n > y = base.ceilb( 3.14159, 0, 2 )\n 4.0\n\n // Round to nearest multiple of two toward positive infinity:\n > y = base.ceilb( 5.0, 1, 2 )\n 6.0\n\n See Also\n --------\n base.ceil, base.ceiln, base.floorb, base.roundb\n","base.ceilf":"\nbase.ceilf( x )\n Rounds a single-precision floating-point number toward positive infinity.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.ceilf( 3.14 )\n 4.0\n > y = base.ceilf( -4.2 )\n -4.0\n > y = base.ceilf( -4.6 )\n -4.0\n > y = base.ceilf( 9.5 )\n 10.0\n > y = base.ceilf( -0.0 )\n -0.0\n\n See Also\n --------\n base.floorf\n","base.ceiln":"\nbase.ceiln( x, n )\n Rounds a numeric value to the nearest multiple of `10^n` toward positive\n infinity.\n\n When operating on floating-point numbers in bases other than `2`, rounding\n to specified digits can be inexact.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Integer power of 10.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n // Round to 2 decimal places:\n > var y = base.ceiln( 3.14159, -2 )\n 3.15\n\n // If `n = 0`, standard round toward positive infinity behavior:\n > y = base.ceiln( 3.14159, 0 )\n 4.0\n\n // Round to nearest thousand:\n > y = base.ceiln( 12368.0, 3 )\n 13000.0\n\n\n See Also\n --------\n base.ceil, base.ceilb, base.floorn, base.roundn\n","base.ceilsd":"\nbase.ceilsd( x, n, b )\n Rounds a numeric value to the nearest number toward positive infinity with\n `n` significant figures.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Number of significant figures. Must be greater than 0.\n\n b: integer\n Base. Must be greater than 0.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.ceilsd( 3.14159, 5, 10 )\n 3.1416\n > y = base.ceilsd( 3.14159, 1, 10 )\n 4.0\n > y = base.ceilsd( 12368.0, 2, 10 )\n 13000.0\n > y = base.ceilsd( 0.0313, 2, 2 )\n 0.046875\n\n See Also\n --------\n base.ceil, base.floorsd, base.roundsd, base.truncsd\n","base.cexp":"\nbase.cexp( z )\n Evaluates the exponential function for a double-precision complex floating-\n point number.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Complex number.\n\n Examples\n --------\n > var y = base.cexp( new Complex128( 0.0, 0.0 ) )\n \n > var re = real( y )\n 1.0\n > var im = imag( y )\n 0.0\n > y = base.cexp( new Complex128( 0.0, 1.0 ) )\n \n > re = real( y )\n ~0.540\n > im = imag( y )\n ~0.841\n\n","base.cflipsign":"\nbase.cflipsign( z, y )\n Returns a double-precision complex floating-point number with the same\n magnitude as `z` and the sign of `y*z`.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n y: number\n Number from which to derive the sign.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var v = base.cflipsign( new Complex128( -4.2, 5.5 ), -9.0 )\n \n > var re = real( v )\n 4.2\n > var im = imag( v )\n -5.5\n\n See Also\n --------\n base.cneg, base.csignum\n","base.cflipsignf":"\nbase.cflipsignf( z, y )\n Returns a single-precision complex floating-point number with the same\n magnitude as `z` and the sign of `y*z`.\n\n Parameters\n ----------\n z: Complex64\n Complex number.\n\n y: number\n Number from which to derive the sign.\n\n Returns\n -------\n out: Complex64\n Result.\n\n Examples\n --------\n > var v = base.cflipsignf( new Complex64( -4.0, 5.0 ), -9.0 )\n \n > var re = realf( v )\n 4.0\n > var im = imagf( v )\n -5.0\n\n See Also\n --------\n base.cnegf, base.cflipsign\n","base.cfloor":"\nbase.cfloor( z )\n Rounds a double-precision complex floating-point number toward negative\n infinity.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var v = base.cfloor( new Complex128( 5.5, 3.3 ) )\n \n > var re = real( v )\n 5.0\n > var im = imag( v )\n 3.0\n\n See Also\n --------\n base.cceil, base.cfloorn, base.cround\n","base.cfloorn":"\nbase.cfloorn( z, n )\n Rounds each component of a double-precision complex floating-point number\n to the nearest multiple of `10^n` toward negative infinity.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n n: integer\n Integer power of 10.\n\n Returns\n -------\n z: Complex128\n Result.\n\n Examples\n --------\n > var v = base.cfloorn( new Complex128( 5.555, -3.333 ), -2 )\n \n > var re = real( v )\n 5.55\n > var im = imag( v )\n -3.34\n\n See Also\n --------\n base.cceiln, base.cfloor, base.croundn\n","base.cidentity":"\nbase.cidentity( z )\n Evaluates the identity function for a double-precision complex floating-\n point number.\n\n Parameters\n ----------\n z: Complex128\n Input value.\n\n Returns\n -------\n v: Complex128\n Input value.\n\n Examples\n --------\n > var v = base.cidentity( new Complex128( -1.0, 2.0 ) )\n \n > var re = real( v )\n -1.0\n > var img = imag( v )\n 2.0\n\n See Also\n --------\n base.cidentityf, base.identity\n","base.cidentityf":"\nbase.cidentityf( z )\n Evaluates the identity function for a single-precision complex floating-\n point number.\n\n Parameters\n ----------\n z: Complex64\n Input value.\n\n Returns\n -------\n v: Complex64\n Input value.\n\n Examples\n --------\n > var v = base.cidentityf( new Complex64( -1.0, 2.0 ) )\n \n > var re = realf( v )\n -1.0\n > var img = imagf( v )\n 2.0\n\n See Also\n --------\n base.cidentity, base.identityf\n","base.cinv":"\nbase.cinv( z )\n Computes the inverse of a double-precision complex floating-point number.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var v = base.cinv( new Complex128( 2.0, 4.0 ) )\n \n > var re = real( v )\n 0.1\n > var im = imag( v )\n -0.2\n\n See Also\n --------\n base.cdiv\n","base.clamp":"\nbase.clamp( v, min, max )\n Restricts a double-precision floating-point number to a specified range.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n v: number\n Value to restrict.\n\n min: number\n Minimum value.\n\n max: number\n Maximum value.\n\n Returns\n -------\n y: number\n Restricted value.\n\n Examples\n --------\n > var y = base.clamp( 3.14, 0.0, 5.0 )\n 3.14\n > y = base.clamp( -3.14, 0.0, 5.0 )\n 0.0\n > y = base.clamp( 3.14, 0.0, 3.0 )\n 3.0\n > y = base.clamp( -0.0, 0.0, 5.0 )\n 0.0\n > y = base.clamp( 0.0, -3.14, -0.0 )\n -0.0\n > y = base.clamp( NaN, 0.0, 5.0 )\n NaN\n\n See Also\n --------\n base.clampf, base.wrap\n","base.clampf":"\nbase.clampf( v, min, max )\n Restricts a single-precision floating-point number to a specified range.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n v: number\n Value to restrict.\n\n min: number\n Minimum value.\n\n max: number\n Maximum value.\n\n Returns\n -------\n y: number\n Restricted value.\n\n Examples\n --------\n > var y = base.clampf( 3.14, 0.0, 5.0 )\n 3.14\n > y = base.clampf( -3.14, 0.0, 5.0 )\n 0.0\n > y = base.clampf( 3.14, 0.0, 3.0 )\n 3.0\n > y = base.clampf( -0.0, 0.0, 5.0 )\n 0.0\n > y = base.clampf( 0.0, -3.14, -0.0 )\n -0.0\n > y = base.clampf( NaN, 0.0, 5.0 )\n NaN\n\n See Also\n --------\n base.clamp\n","base.cmul":"\nbase.cmul( z1, z2 )\n Multiplies two double-precision complex floating-point numbers.\n\n Parameters\n ----------\n z1: Complex128\n Complex number.\n\n z2: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var z1 = new Complex128( 5.0, 3.0 )\n \n > var z2 = new Complex128( -2.0, 1.0 )\n \n > var out = base.cmul( z1, z2 )\n \n > var re = real( out )\n -13.0\n > var im = imag( out )\n -1.0\n\n See Also\n --------\n base.cadd, base.cdiv, base.csub\n","base.cmulf":"\nbase.cmulf( z1, z2 )\n Multiplies two single-precision complex floating-point numbers.\n\n Parameters\n ----------\n z1: Complex64\n Complex number.\n\n z2: Complex64\n Complex number.\n\n Returns\n -------\n out: Complex64\n Result.\n\n Examples\n --------\n > var z1 = new Complex64( 5.0, 3.0 )\n \n > var z2 = new Complex64( -2.0, 1.0 )\n \n > var out = base.cmulf( z1, z2 )\n \n > var re = realf( out )\n -13.0\n > var im = imagf( out )\n -1.0\n\n See Also\n --------\n base.caddf, base.cmul, base.csubf\n","base.cneg":"\nbase.cneg( z )\n Negates a double-precision complex floating-point number.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var z = new Complex128( -4.2, 5.5 )\n \n > var v = base.cneg( z )\n \n > var re = real( v )\n 4.2\n > var im = imag( v )\n -5.5\n\n See Also\n --------\n base.cabs\n","base.cnegf":"\nbase.cnegf( z )\n Negates a single-precision complex floating-point number.\n\n Parameters\n ----------\n z: Complex64\n Complex number.\n\n Returns\n -------\n out: Complex64\n Result.\n\n Examples\n --------\n > var z = new Complex64( -4.0, 5.0 )\n \n > var v = base.cnegf( z )\n \n > var re = realf( v )\n 4.0\n > var im = imagf( v )\n -5.0\n\n See Also\n --------\n base.cneg, base.cabsf\n","base.codePointAt":"\nbase.codePointAt( str, idx, backward )\n Returns a Unicode code point from a string at a specified position.\n\n Parameters\n ----------\n str: string\n Input string.\n\n idx: integer\n Position. If less than `0`, the string position is determined relative\n to the end of the input string.\n\n backward: boolean\n Backward iteration for low surrogates.\n\n Returns\n -------\n out: integer\n Unicode code point.\n\n Examples\n --------\n > var out = base.codePointAt( 'last man standing', 4, false )\n 32\n > out = base.codePointAt( 'presidential election', 8, true )\n 116\n > out = base.codePointAt( 'अनुच्छेद', 2, false )\n 2369\n > out = base.codePointAt( '🌷', 1, true )\n 127799\n","base.constantcase":"\nbase.constantcase( str )\n Converts a string to constant case.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Constant-cased string.\n\n Examples\n --------\n > var out = base.constantcase( 'Hello World!' )\n 'HELLO_WORLD'\n > out = base.constantcase( 'I am a tiny little teapot' )\n 'I_AM_A_TINY_LITTLE_TEAPOT'\n\n See Also\n --------\n base.camelcase, base.lowercase, base.snakecase, base.uppercase","base.continuedFraction":"\nbase.continuedFraction( generator[, options] )\n Evaluates the continued fraction approximation for the supplied series\n generator using the modified Lentz algorithm.\n\n `generator` can be either a function which returns an array with two\n elements, the `a` and `b` terms of the fraction, or an ES6 Generator object.\n\n By default, the function computes\n\n a1\n ---------------\n b1 + a2\n ----------\n b2 + a3\n -----\n b3 + ...\n\n To evaluate\n\n b0 +\t a1\n ---------------\n b1 +\t a2\n ----------\n b2 + a3\n -----\n b3 + ...\n\n set the `keep` option to `true`.\n\n Parameters\n ----------\n generator: Function\n Function returning terms of continued fraction expansion.\n\n options: Object (optional)\n Options.\n\n options.maxIter: integer (optional)\n Maximum number of iterations. Default: `1000000`.\n\n options.tolerance: number (optional)\n Further terms are only added as long as the next term is greater than\n current term times the tolerance. Default: `2.22e-16`.\n\n options.keep: boolean (optional)\n Boolean indicating whether to keep the `b0` term in the continued\n fraction. Default: `false`.\n\n Returns\n -------\n out: number\n Value of continued fraction.\n\n Examples\n --------\n // Continued fraction for (e-1)^(-1):\n > function closure() {\n ... var i = 0;\n ... return function() {\n ... i += 1;\n ... return [ i, i ];\n ... };\n ... };\n > var gen = closure();\n > var out = base.continuedFraction( gen )\n ~0.582\n\n // Using an ES6 generator:\n > function* generator() {\n ... var i = 0;\n ... while ( true ) {\n ... i += 1;\n ... yield [ i, i ];\n ... }\n ... };\n > gen = generator();\n > out = base.continuedFraction( gen )\n ~0.582\n\n // Set options:\n > out = base.continuedFraction( generator(), { 'keep': true } )\n ~1.718\n > out = base.continuedFraction( generator(), { 'maxIter': 10 } )\n ~0.582\n > out = base.continuedFraction( generator(), { 'tolerance': 1e-1 } )\n ~0.579\n\n","base.copysign":"\nbase.copysign( x, y )\n Returns a double-precision floating-point number with the magnitude of `x`\n and the sign of `y`.\n\n Parameters\n ----------\n x: number\n Number from which to derive a magnitude.\n\n y: number\n Number from which to derive a sign.\n\n Returns\n -------\n z: number\n Double-precision floating-point number.\n\n Examples\n --------\n > var z = base.copysign( -3.14, 10.0 )\n 3.14\n > z = base.copysign( 3.14, -1.0 )\n -3.14\n > z = base.copysign( 1.0, -0.0 )\n -1.0\n > z = base.copysign( -3.14, -0.0 )\n -3.14\n > z = base.copysign( -0.0, 1.0 )\n 0.0\n\n See Also\n --------\n base.flipsign\n","base.copysignf":"\nbase.copysignf( x, y )\n Returns a single-precision floating-point number with the magnitude of `x`\n and the sign of `y`.\n\n Parameters\n ----------\n x: number\n Number from which to derive a magnitude.\n\n y: number\n Number from which to derive a sign.\n\n Returns\n -------\n z: number\n Single-precision floating-point number.\n\n Examples\n --------\n > var z = base.copysignf( -3.0, 10.0 )\n 3.0\n > z = base.copysignf( 3.0, -1.0 )\n -3.0\n > z = base.copysignf( 1.0, -0.0 )\n -1.0\n > z = base.copysignf( -3.0, -0.0 )\n -3.0\n > z = base.copysignf( -0.0, 1.0 )\n 0.0\n\n See Also\n --------\n base.copysign, base.flipsignf\n","base.cos":"\nbase.cos( x )\n Computes the cosine of a number.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Cosine.\n\n Examples\n --------\n > var y = base.cos( 0.0 )\n 1.0\n > y = base.cos( PI/4.0 )\n ~0.707\n > y = base.cos( -PI/6.0 )\n ~0.866\n > y = base.cos( NaN )\n NaN\n\n See Also\n --------\n base.cospi, base.cosm1, base.sin, base.tan\n","base.cosd":"\nbase.cosd( x )\n Computes the cosine of an angle measured in degrees.\n\n Parameters\n ----------\n x: number\n Input value (in degrees).\n\n Returns\n -------\n y: number\n Cosine.\n\n Examples\n --------\n > var y = base.cosd( 0.0 )\n 1.0\n > y = base.cosd( 90.0 )\n 0.0\n > y = base.cosd( 60.0 )\n ~0.5\n > y = base.cosd( NaN )\n NaN\n\n See Also\n --------\n base.tand\n","base.cosh":"\nbase.cosh( x )\n Computes the hyperbolic cosine of a double-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Hyperbolic cosine.\n\n Examples\n --------\n > var y = base.cosh( 0.0 )\n 1.0\n > y = base.cosh( 2.0 )\n ~3.762\n > y = base.cosh( -2.0 )\n ~3.762\n > y = base.cosh( NaN )\n NaN\n\n See Also\n --------\n base.cos, base.sinh, base.tanh\n","base.cosm1":"\nbase.cosm1( x )\n Computes the cosine of a number minus one.\n\n This function should be used instead of manually calculating `cos(x)-1` when\n `x` is near unity.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Cosine minus one.\n\n Examples\n --------\n > var y = base.cosm1( 0.0 )\n 0.0\n > y = base.cosm1( PI/4.0 )\n ~-0.293\n > y = base.cosm1( -PI/6.0 )\n ~-0.134\n > y = base.cosm1( NaN )\n NaN\n\n See Also\n --------\n base.cos\n","base.cospi":"\nbase.cospi( x )\n Computes the value of `cos(πx)`.\n\n This function computes `cos(πx)` more accurately than the obvious approach,\n especially for large `x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.cospi( 0.0 )\n 1.0\n > y = base.cospi( 0.5 )\n 0.0\n > y = base.cospi( 0.1 )\n ~0.951\n > y = base.cospi( NaN )\n NaN\n\n See Also\n --------\n base.cos\n","base.cot":"\nbase.cot( x )\n Computes the cotangent of a number.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Cotangent.\n\n Examples\n --------\n > var y = base.cot( 0.0 )\n Infinity\n > y = base.cot( -PI/4.0 )\n ~-1.0\n > y = base.cot( PI/4.0 )\n ~1.0\n > y = base.cot( NaN )\n NaN\n\n See Also\n --------\n base.csc, base.tan\n","base.cotd":"\nbase.cotd( x )\n Computes the cotangent of an angle measured in degrees.\n\n Parameters\n ----------\n x: number\n Input value (in degrees).\n\n Returns\n -------\n y: number\n Cotangent.\n\n Examples\n --------\n > var y = base.cotd( 0.0 )\n Infinity\n > y = base.cotd( 90.0 )\n 0.0\n > y = base.cotd( 60.0 )\n ~0.58\n > y = base.cotd( NaN )\n NaN\n\n See Also\n --------\n base.cscd, base.secd, base.tand\n","base.coth":"\nbase.coth( x )\n Computes the hyperbolic cotangent of a number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Hyperbolic cotangent.\n\n Examples\n --------\n > var y = base.coth( 0.0 )\n Infinity\n > y = base.coth( -0.0 )\n -Infinity\n > y = base.coth( 2.0 )\n ~1.0373\n > y = base.coth( -2.0 )\n ~-1.0373\n > y = base.coth( +Infinity )\n ~1\n > y = base.coth( -Infinity )\n ~-1\n > y = base.coth( NaN )\n NaN\n\n See Also\n --------\n base.acoth, base.cot, base.csch, base.tanh\n","base.covercos":"\nbase.covercos( x )\n Computes the coversed cosine.\n\n The coversed cosine is defined as `1 + sin(x)`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Coversed cosine.\n\n Examples\n --------\n > var y = base.covercos( 3.14 )\n ~1.0016\n > y = base.covercos( -4.2 )\n ~1.8716\n > y = base.covercos( -4.6 )\n ~1.9937\n > y = base.covercos( 9.5 )\n ~0.9248\n > y = base.covercos( -0.0 )\n 1.0\n\n See Also\n --------\n base.coversin, base.vercos\n","base.coversin":"\nbase.coversin( x )\n Computes the coversed sine.\n\n The coversed sine is defined as `1 - sin(x)`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Coversed sine.\n\n Examples\n --------\n > var y = base.coversin( 3.14 )\n ~0.9984\n > y = base.coversin( -4.2 )\n ~0.1284\n > y = base.coversin( -4.6 )\n ~0.0063\n > y = base.coversin( 9.5 )\n ~1.0752\n > y = base.coversin( -0.0 )\n 1.0\n\n See Also\n --------\n base.covercos, base.versin\n","base.cphase":"\nbase.cphase( z )\n Computes the argument of a double-precision complex floating-point number\n in radians.\n\n The argument of a complex number, also known as the phase, is the angle of\n the radius extending from the origin to the complex number plotted in the\n complex plane and the positive real axis.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n phi: number\n Argument.\n\n Examples\n --------\n > var phi = base.cphase( new Complex128( 5.0, 3.0 ) )\n ~0.5404\n\n See Also\n --------\n base.cabs\n","base.cpolar":"\nbase.cpolar( z )\n Returns the absolute value and phase of a double-precision complex\n floating-point number.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n out: Array\n Absolute value and phase, respectively.\n\n Examples\n --------\n > var out = base.cpolar( new Complex128( 5.0, 3.0 ) )\n [ ~5.83, ~0.5404 ]\n\n\nbase.cpolar.assign( z, out, stride, offset )\n Returns the absolute value and phase of a double-precision complex\n floating-point number and assigns results to a provided output array.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n out: Array|TypedArray|Object\n Destination array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Absolute value and phase, respectively.\n\n Examples\n --------\n > var out = new Float64Array( 2 );\n > var v = base.cpolar.assign( new Complex128( 5.0, 3.0 ), out, 1, 0 )\n [ ~5.83, ~0.5404 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.cabs, base.cphase","base.cpolar.assign":"\nbase.cpolar.assign( z, out, stride, offset )\n Returns the absolute value and phase of a double-precision complex\n floating-point number and assigns results to a provided output array.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n out: Array|TypedArray|Object\n Destination array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Absolute value and phase, respectively.\n\n Examples\n --------\n > var out = new Float64Array( 2 );\n > var v = base.cpolar.assign( new Complex128( 5.0, 3.0 ), out, 1, 0 )\n [ ~5.83, ~0.5404 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.cabs, base.cphase","base.cround":"\nbase.cround( z )\n Rounds each component of a double-precision complex floating-point number\n to the nearest integer.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Rounded complex number.\n\n Examples\n --------\n > var v = base.cround( new Complex128( 5.5, 3.3 ) )\n \n > var re = real( v )\n 6.0\n > var im = imag( v )\n 3.0\n\n See Also\n --------\n base.cceil, base.cfloor, base.croundn\n","base.croundn":"\nbase.croundn( z, n )\n Rounds each component of a double-precision complex floating-point number\n to the nearest multiple of `10^n`.\n\n When operating on floating-point numbers in bases other than `2`, rounding\n to specified digits can be inexact.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n n: integer\n Integer power of 10.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var v = base.croundn( new Complex128( 5.555, -3.336 ), -2 )\n \n > var re = real( v )\n 5.56\n > var im = imag( v )\n -3.34\n\n See Also\n --------\n base.cceiln, base.cfloorn, base.cround\n","base.csc":"\nbase.csc( x )\n Computes the cosecant of a number.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Cosecant.\n\n Examples\n --------\n > var y = base.csc( 0.0 )\n Infinity\n > y = base.csc( PI/2.0 )\n ~1.0\n > y = base.csc( -PI/6.0 )\n ~-2.0\n > y = base.csc( NaN )\n NaN\n\n See Also\n --------\n base.cot, base.sin","base.cscd":"\nbase.cscd( x )\n Computes the cosecant of a degree.\n\n Parameters\n ----------\n x: number\n Input value (in degrees).\n\n Returns\n -------\n y: number\n Cosecant.\n\n Examples\n --------\n > var y = base.cscd( 1.0 )\n ~57.30\n > y = base.cscd( PI )\n ~18.25\n > y = base.cscd( -PI )\n ~-18.25\n > y = base.cscd( NaN )\n NaN\n\n See Also\n --------\n base.cotd, base.secd\n","base.csch":"\nbase.csch( x )\n Computes the hyperbolic cosecant of a number.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Hyperbolic cosecant.\n\n Examples\n --------\n > var y = base.csch( +0.0 )\n +Infinity\n > var y = base.csch( -0.0 )\n -Infinity\n > var y = base.csch( +Infinity )\n +0.0\n > var y = base.csch( -Infinity )\n -0.0\n > y = base.csch( 2.0 )\n ~0.2757\n > y = base.csch( -2.0 )\n ~-0.2757\n > y = base.csch( NaN )\n NaN\n\n See Also\n --------\n base.acsch, base.csc, base.coth, base.sinh\n","base.csignum":"\nbase.csignum( z )\n Evaluates the signum function of a double-precision complex floating-point\n number.\n\n Parameters\n ----------\n z: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var v = base.csignum( new Complex128( -4.2, 5.5 ) )\n \n > var re = real( v )\n -0.6069136033622302\n > var im = imag( v )\n 0.79476781392673\n\n See Also\n --------\n base.signum\n","base.csub":"\nbase.csub( z1, z2 )\n Subtracts two double-precision complex floating-point numbers.\n\n Parameters\n ----------\n z1: Complex128\n Complex number.\n\n z2: Complex128\n Complex number.\n\n Returns\n -------\n out: Complex128\n Result.\n\n Examples\n --------\n > var z1 = new Complex128( 5.0, 3.0 )\n \n > var z2 = new Complex128( -2.0, 1.0 )\n \n > var out = base.csub( z1, z2 )\n \n > var re = real( out )\n 7.0\n > var im = imag( out )\n 2.0\n\n See Also\n --------\n base.cadd, base.cdiv, base.cmul\n","base.csubf":"\nbase.csubf( z1, z2 )\n Subtracts two single-precision complex floating-point numbers.\n\n Parameters\n ----------\n z1: Complex64\n Complex number.\n\n z2: Complex64\n Complex number.\n\n Returns\n -------\n out: Complex64\n Result.\n\n Examples\n --------\n > var z1 = new Complex64( 5.0, 3.0 )\n \n > var z2 = new Complex64( -2.0, 1.0 )\n \n > var out = base.csubf( z1, z2 )\n \n > var re = realf( out )\n 7.0\n > var im = imagf( out )\n 2.0\n\n See Also\n --------\n base.caddf, base.cmulf, base.csub\n","base.deg2rad":"\nbase.deg2rad( x )\n Converts an angle from degrees to radians.\n\n Parameters\n ----------\n x: number\n Angle in degrees.\n\n Returns\n -------\n r: number\n Angle in radians.\n\n Examples\n --------\n > var r = base.deg2rad( 90.0 )\n ~1.571\n > r = base.deg2rad( -45.0 )\n ~-0.785\n > r = base.deg2rad( NaN )\n NaN\n\n See Also\n --------\n base.rad2deg\n","base.deg2radf":"\nbase.deg2radf( x )\n Converts an angle from degrees to radians (single-precision).\n\n Parameters\n ----------\n x: number\n Angle in degrees.\n\n Returns\n -------\n r: number\n Angle in radians.\n\n Examples\n --------\n > var r = base.deg2radf( 90.0 )\n ~1.571\n > r = base.deg2radf( -45.0 )\n ~-0.785\n > r = base.deg2radf( NaN )\n NaN\n\n See Also\n --------\n base.deg2rad, base.rad2degf\n","base.digamma":"\nbase.digamma( x )\n Evaluates the digamma function.\n\n If `x` is zero or a negative integer, the function returns `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.digamma( -2.5 )\n ~1.103\n > y = base.digamma( 1.0 )\n ~-0.577\n > y = base.digamma( 10.0 )\n ~2.252\n > y = base.digamma( NaN )\n NaN\n > y = base.digamma( -1.0 )\n NaN\n\n See Also\n --------\n base.gamma, base.trigamma\n","base.diracDelta":"\nbase.diracDelta( x )\n Evaluates the Dirac delta function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.diracDelta( 3.14 )\n 0.0\n > y = base.diracDelta( 0.0 )\n Infinity\n\n See Also\n --------\n base.kroneckerDelta\n","base.div":"\nbase.div( x, y )\n Divides two double-precision floating-point numbers `x` and `y`.\n\n Parameters\n ----------\n x: number\n First input value (dividend).\n\n y: number\n Second input value (divisor).\n\n Returns\n -------\n z: number\n Result.\n\n Examples\n --------\n > var v = base.div( -1.0, 5.0 )\n -0.2\n > v = base.div( 2.0, 5.0 )\n 0.4\n > v = base.div( 0.0, 5.0 )\n 0.0\n > v = base.div( -0.0, 5.0 )\n -0.0\n > v = base.div( NaN, NaN )\n NaN\n\n See Also\n --------\n base.add, base.mul, base.sub\n","base.divf":"\nbase.divf( x, y )\n Divides two single-precision floating-point numbers `x` and `y`.\n\n Parameters\n ----------\n x: number\n First input value (dividend).\n\n y: number\n Second input value (divisor).\n\n Returns\n -------\n z: number\n Result.\n\n Examples\n --------\n > var v = base.divf( -1.0, 5.0 )\n ~-0.2\n > v = base.divf( 2.0, 5.0 )\n ~0.4\n > v = base.divf( 0.0, 5.0 )\n 0.0\n > v = base.divf( -0.0, 5.0 )\n -0.0\n > v = base.divf( NaN, NaN )\n NaN\n\n See Also\n --------\n base.addf, base.div, base.mulf, base.subf\n","base.dotcase":"\nbase.dotcase( str )\n Converts a string to dot case.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Dot-cased string.\n\n Examples\n --------\n > var out = base.dotcase( 'Hello World!' )\n 'hello.world'\n > out = base.dotcase( 'I am a tiny little teapot' )\n 'i.am.a.tiny.little.teapot'\n\n See Also\n --------\n base.camelcase, base.lowercase, base.snakecase, base.uppercase","base.dists.arcsine.Arcsine":"\nbase.dists.arcsine.Arcsine( [a, b] )\n Returns an arcsine distribution object.\n\n Parameters\n ----------\n a: number (optional)\n Minimum support. Must be less than `b`. Default: `0.0`.\n\n b: number (optional)\n Maximum support. Must be greater than `a`. Default: `1.0`.\n\n Returns\n -------\n arcsine: Object\n Distribution instance.\n\n arcsine.a: number\n Minimum support. If set, the value must be less than `b`.\n\n arcsine.b: number\n Maximum support. If set, the value must be greater than `a`.\n\n arcsine.entropy: number\n Read-only property which returns the differential entropy.\n\n arcsine.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n arcsine.mean: number\n Read-only property which returns the expected value.\n\n arcsine.median: number\n Read-only property which returns the median.\n\n arcsine.mode: number\n Read-only property which returns the mode.\n\n arcsine.skewness: number\n Read-only property which returns the skewness.\n\n arcsine.stdev: number\n Read-only property which returns the standard deviation.\n\n arcsine.variance: number\n Read-only property which returns the variance.\n\n arcsine.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n arcsine.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n arcsine.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n arcsine.pdf: Function\n Evaluates the probability density function (PDF).\n\n arcsine.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var arcsine = base.dists.arcsine.Arcsine( 0.0, 1.0 );\n > arcsine.a\n 0.0\n > arcsine.b\n 1.0\n > arcsine.entropy\n ~-0.242\n > arcsine.kurtosis\n -1.5\n > arcsine.mean\n 0.5\n > arcsine.median\n 0.5\n > arcsine.mode\n 0.0\n > arcsine.skewness\n 0.0\n > arcsine.stdev\n ~0.354\n > arcsine.variance\n 0.125\n > arcsine.cdf( 0.8 )\n ~0.705\n > arcsine.logcdf( 0.8 )\n ~-0.35\n > arcsine.logpdf( 0.4 )\n ~-0.431\n > arcsine.pdf( 0.8 )\n ~0.796\n > arcsine.quantile( 0.8 )\n ~0.905\n\n","base.dists.arcsine.cdf":"\nbase.dists.arcsine.cdf( x, a, b )\n Evaluates the cumulative distribution function (CDF) for an arcsine\n distribution with minimum support `a` and maximum support `b` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.arcsine.cdf( 9.0, 0.0, 10.0 )\n ~0.795\n > y = base.dists.arcsine.cdf( 0.5, 0.0, 2.0 )\n ~0.333\n > y = base.dists.arcsine.cdf( PINF, 2.0, 4.0 )\n 1.0\n > y = base.dists.arcsine.cdf( NINF, 2.0, 4.0 )\n 0.0\n > y = base.dists.arcsine.cdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.arcsine.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.arcsine.cdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.arcsine.cdf( 2.0, 1.0, 0.0 )\n NaN\n\n\nbase.dists.arcsine.cdf.factory( a, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of an arcsine distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.arcsine.cdf.factory( 0.0, 10.0 );\n > var y = mycdf( 0.5 )\n ~0.144\n > y = mycdf( 8.0 )\n ~0.705\n\n","base.dists.arcsine.cdf.factory":"\nbase.dists.arcsine.cdf.factory( a, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of an arcsine distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.arcsine.cdf.factory( 0.0, 10.0 );\n > var y = mycdf( 0.5 )\n ~0.144\n > y = mycdf( 8.0 )\n ~0.705","base.dists.arcsine.entropy":"\nbase.dists.arcsine.entropy( a, b )\n Returns the differential entropy of an arcsine distribution (in nats).\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.arcsine.entropy( 0.0, 1.0 )\n ~-0.242\n > v = base.dists.arcsine.entropy( 4.0, 12.0 )\n ~1.838\n > v = base.dists.arcsine.entropy( 2.0, 8.0 )\n ~1.55\n\n","base.dists.arcsine.kurtosis":"\nbase.dists.arcsine.kurtosis( a, b )\n Returns the excess kurtosis of an arcsine distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.arcsine.kurtosis( 0.0, 1.0 )\n -1.5\n > v = base.dists.arcsine.kurtosis( 4.0, 12.0 )\n -1.5\n > v = base.dists.arcsine.kurtosis( 2.0, 8.0 )\n -1.5\n\n","base.dists.arcsine.logcdf":"\nbase.dists.arcsine.logcdf( x, a, b )\n Evaluates the logarithm of the cumulative distribution function (CDF) for an\n arcsine distribution with minimum support `a` and maximum support `b` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.arcsine.logcdf( 9.0, 0.0, 10.0 )\n ~-0.229\n > y = base.dists.arcsine.logcdf( 0.5, 0.0, 2.0 )\n ~-1.1\n > y = base.dists.arcsine.logcdf( PINF, 2.0, 4.0 )\n 0.0\n > y = base.dists.arcsine.logcdf( NINF, 2.0, 4.0 )\n -Infinity\n > y = base.dists.arcsine.logcdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.arcsine.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.arcsine.logcdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.arcsine.logcdf( 2.0, 1.0, 0.0 )\n NaN\n\n\nbase.dists.arcsine.logcdf.factory( a, b )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of an arcsine distribution with minimum support\n `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.arcsine.logcdf.factory( 0.0, 10.0 );\n > var y = mylogcdf( 0.5 )\n ~-1.941\n > y = mylogcdf( 8.0 )\n ~-0.35\n\n","base.dists.arcsine.logcdf.factory":"\nbase.dists.arcsine.logcdf.factory( a, b )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of an arcsine distribution with minimum support\n `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.arcsine.logcdf.factory( 0.0, 10.0 );\n > var y = mylogcdf( 0.5 )\n ~-1.941\n > y = mylogcdf( 8.0 )\n ~-0.35","base.dists.arcsine.logpdf":"\nbase.dists.arcsine.logpdf( x, a, b )\n Evaluates the logarithm of the probability density function (PDF) for an\n arcsine distribution with minimum support `a` and maximum support `b` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.arcsine.logpdf( 2.0, 0.0, 4.0 )\n ~-1.838\n > y = base.dists.arcsine.logpdf( 5.0, 0.0, 4.0 )\n -Infinity\n > y = base.dists.arcsine.logpdf( 0.25, 0.0, 1.0 )\n ~-0.308\n > y = base.dists.arcsine.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.arcsine.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.arcsine.logpdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.arcsine.logpdf( 2.0, 3.0, 1.0 )\n NaN\n\n\nbase.dists.arcsine.logpdf.factory( a, b )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of an arcsine distribution with minimum support `a` and\n maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.arcsine.logpdf.factory( 6.0, 7.0 );\n > var y = mylogPDF( 7.0 )\n Infinity\n > y = mylogPDF( 5.0 )\n -Infinity\n\n","base.dists.arcsine.logpdf.factory":"\nbase.dists.arcsine.logpdf.factory( a, b )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of an arcsine distribution with minimum support `a` and\n maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.arcsine.logpdf.factory( 6.0, 7.0 );\n > var y = mylogPDF( 7.0 )\n Infinity\n > y = mylogPDF( 5.0 )\n -Infinity","base.dists.arcsine.mean":"\nbase.dists.arcsine.mean( a, b )\n Returns the expected value of an arcsine distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.arcsine.mean( 0.0, 1.0 )\n 0.5\n > v = base.dists.arcsine.mean( 4.0, 12.0 )\n 8.0\n > v = base.dists.arcsine.mean( 2.0, 8.0 )\n 5.0\n\n","base.dists.arcsine.median":"\nbase.dists.arcsine.median( a, b )\n Returns the median of an arcsine distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.arcsine.median( 0.0, 1.0 )\n 0.5\n > v = base.dists.arcsine.median( 4.0, 12.0 )\n 8.0\n > v = base.dists.arcsine.median( 2.0, 8.0 )\n 5.0\n\n","base.dists.arcsine.mode":"\nbase.dists.arcsine.mode( a, b )\n Returns the mode of an arcsine distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.arcsine.mode( 0.0, 1.0 )\n 0.0\n > v = base.dists.arcsine.mode( 4.0, 12.0 )\n 4.0\n > v = base.dists.arcsine.mode( 2.0, 8.0 )\n 2.0\n\n","base.dists.arcsine.pdf":"\nbase.dists.arcsine.pdf( x, a, b )\n Evaluates the probability density function (PDF) for an arcsine distribution\n with minimum support `a` and maximum support `b` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.arcsine.pdf( 2.0, 0.0, 4.0 )\n ~0.159\n > y = base.dists.arcsine.pdf( 5.0, 0.0, 4.0 )\n 0.0\n > y = base.dists.arcsine.pdf( 0.25, 0.0, 1.0 )\n ~0.735\n > y = base.dists.arcsine.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.arcsine.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.arcsine.pdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.arcsine.pdf( 2.0, 3.0, 1.0 )\n NaN\n\n\nbase.dists.arcsine.pdf.factory( a, b )\n Returns a function for evaluating the probability density function (PDF) of\n an arcsine distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.arcsine.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 7.0 )\n Infinity\n > y = myPDF( 5.0 )\n 0.0\n\n","base.dists.arcsine.pdf.factory":"\nbase.dists.arcsine.pdf.factory( a, b )\n Returns a function for evaluating the probability density function (PDF) of\n an arcsine distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.arcsine.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 7.0 )\n Infinity\n > y = myPDF( 5.0 )\n 0.0","base.dists.arcsine.quantile":"\nbase.dists.arcsine.quantile( p, a, b )\n Evaluates the quantile function for an arcsine distribution with minimum\n support `a` and maximum support `b` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.arcsine.quantile( 0.8, 0.0, 1.0 )\n ~0.905\n > y = base.dists.arcsine.quantile( 0.5, 0.0, 10.0 )\n ~5.0\n\n > y = base.dists.arcsine.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.arcsine.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.arcsine.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.arcsine.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.arcsine.quantile( 0.0, 0.0, NaN )\n NaN\n\n > y = base.dists.arcsine.quantile( 0.5, 2.0, 1.0 )\n NaN\n\n\nbase.dists.arcsine.quantile.factory( a, b )\n Returns a function for evaluating the quantile function of an arcsine\n distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.arcsine.quantile.factory( 0.0, 4.0 );\n > var y = myQuantile( 0.8 )\n ~3.618\n\n","base.dists.arcsine.quantile.factory":"\nbase.dists.arcsine.quantile.factory( a, b )\n Returns a function for evaluating the quantile function of an arcsine\n distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.arcsine.quantile.factory( 0.0, 4.0 );\n > var y = myQuantile( 0.8 )\n ~3.618","base.dists.arcsine.skewness":"\nbase.dists.arcsine.skewness( a, b )\n Returns the skewness of an arcsine distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.arcsine.skewness( 0.0, 1.0 )\n 0.0\n > v = base.dists.arcsine.skewness( 4.0, 12.0 )\n 0.0\n > v = base.dists.arcsine.skewness( 2.0, 8.0 )\n 0.0\n\n","base.dists.arcsine.stdev":"\nbase.dists.arcsine.stdev( a, b )\n Returns the standard deviation of an arcsine distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.arcsine.stdev( 0.0, 1.0 )\n ~0.354\n > v = base.dists.arcsine.stdev( 4.0, 12.0 )\n ~2.828\n > v = base.dists.arcsine.stdev( 2.0, 8.0 )\n ~2.121\n\n","base.dists.arcsine.variance":"\nbase.dists.arcsine.variance( a, b )\n Returns the variance of an arcsine distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.arcsine.variance( 0.0, 1.0 )\n ~0.125\n > v = base.dists.arcsine.variance( 4.0, 12.0 )\n 8.0\n > v = base.dists.arcsine.variance( 2.0, 8.0 )\n ~4.5\n\n","base.dists.bernoulli.Bernoulli":"\nbase.dists.bernoulli.Bernoulli( [p] )\n Returns a Bernoulli distribution object.\n\n Parameters\n ----------\n p: number (optional)\n Success probability. Must be between `0` and `1`. Default: `0.5`.\n\n Returns\n -------\n bernoulli: Object\n Distribution instance.\n\n bernoulli.p: number\n Success probability. If set, the value must be between `0` and `1`.\n\n bernoulli.entropy: number\n Read-only property which returns the differential entropy.\n\n bernoulli.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n bernoulli.mean: number\n Read-only property which returns the expected value.\n\n bernoulli.median: number\n Read-only property which returns the median.\n\n bernoulli.skewness: number\n Read-only property which returns the skewness.\n\n bernoulli.stdev: number\n Read-only property which returns the standard deviation.\n\n bernoulli.variance: number\n Read-only property which returns the variance.\n\n bernoulli.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n bernoulli.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n bernoulli.pmf: Function\n Evaluates the probability mass function (PMF).\n\n bernoulli.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var bernoulli = base.dists.bernoulli.Bernoulli( 0.6 );\n > bernoulli.p\n 0.6\n > bernoulli.entropy\n ~0.673\n > bernoulli.kurtosis\n ~-1.833\n > bernoulli.mean\n 0.6\n > bernoulli.median\n 1.0\n > bernoulli.skewness\n ~-0.408\n > bernoulli.stdev\n ~0.49\n > bernoulli.variance\n ~0.24\n > bernoulli.cdf( 0.5 )\n 0.4\n > bernoulli.mgf( 3.0 )\n ~12.451\n > bernoulli.pmf( 0.0 )\n 0.4\n > bernoulli.quantile( 0.7 )\n 1.0\n\n","base.dists.bernoulli.cdf":"\nbase.dists.bernoulli.cdf( x, p )\n Evaluates the cumulative distribution function (CDF) for a Bernoulli\n distribution with success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.bernoulli.cdf( 0.5, 0.5 )\n 0.5\n > y = base.dists.bernoulli.cdf( 0.8, 0.1 )\n 0.9\n > y = base.dists.bernoulli.cdf( -1.0, 0.4 )\n 0.0\n > y = base.dists.bernoulli.cdf( 1.5, 0.4 )\n 1.0\n > y = base.dists.bernoulli.cdf( NaN, 0.5 )\n NaN\n > y = base.dists.bernoulli.cdf( 0.0, NaN )\n NaN\n // Invalid probability:\n > y = base.dists.bernoulli.cdf( 2.0, 1.4 )\n NaN\n\n\nbase.dists.bernoulli.cdf.factory( p )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Bernoulli distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.bernoulli.cdf.factory( 0.5 );\n > var y = mycdf( 3.0 )\n 1.0\n > y = mycdf( 0.7 )\n 0.5\n\n","base.dists.bernoulli.cdf.factory":"\nbase.dists.bernoulli.cdf.factory( p )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Bernoulli distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.bernoulli.cdf.factory( 0.5 );\n > var y = mycdf( 3.0 )\n 1.0\n > y = mycdf( 0.7 )\n 0.5","base.dists.bernoulli.entropy":"\nbase.dists.bernoulli.entropy( p )\n Returns the entropy of a Bernoulli distribution with success probability\n `p` (in nats).\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.bernoulli.entropy( 0.1 )\n ~0.325\n > v = base.dists.bernoulli.entropy( 0.5 )\n ~0.693\n\n","base.dists.bernoulli.kurtosis":"\nbase.dists.bernoulli.kurtosis( p )\n Returns the excess kurtosis of a Bernoulli distribution with success\n probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.bernoulli.kurtosis( 0.1 )\n ~5.111\n > v = base.dists.bernoulli.kurtosis( 0.5 )\n -2.0\n\n","base.dists.bernoulli.mean":"\nbase.dists.bernoulli.mean( p )\n Returns the expected value of a Bernoulli distribution with success\n probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.bernoulli.mean( 0.1 )\n 0.1\n > v = base.dists.bernoulli.mean( 0.5 )\n 0.5\n\n","base.dists.bernoulli.median":"\nbase.dists.bernoulli.median( p )\n Returns the median of a Bernoulli distribution with success probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: integer\n Median.\n\n Examples\n --------\n > var v = base.dists.bernoulli.median( 0.1 )\n 0\n > v = base.dists.bernoulli.median( 0.8 )\n 1\n\n","base.dists.bernoulli.mgf":"\nbase.dists.bernoulli.mgf( t, p )\n Evaluates the moment-generating function (MGF) for a Bernoulli\n distribution with success probability `p` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.bernoulli.mgf( 0.2, 0.5 )\n ~1.111\n > y = base.dists.bernoulli.mgf( 0.4, 0.5 )\n ~1.246\n > y = base.dists.bernoulli.mgf( NaN, 0.0 )\n NaN\n > y = base.dists.bernoulli.mgf( 0.0, NaN )\n NaN\n > y = base.dists.bernoulli.mgf( -2.0, -1.0 )\n NaN\n > y = base.dists.bernoulli.mgf( 0.2, 2.0 )\n NaN\n\n\nbase.dists.bernoulli.mgf.factory( p )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Bernoulli distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.bernoulli.mgf.factory( 0.8 );\n > var y = mymgf( -0.2 )\n ~0.855\n\n","base.dists.bernoulli.mgf.factory":"\nbase.dists.bernoulli.mgf.factory( p )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Bernoulli distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.bernoulli.mgf.factory( 0.8 );\n > var y = mymgf( -0.2 )\n ~0.855","base.dists.bernoulli.mode":"\nbase.dists.bernoulli.mode( p )\n Returns the mode of a Bernoulli distribution with success probability `p`.\n\n For `p = 0.5`, the mode is either `0` or `1`. This implementation returns\n `0` for `p = 0.5`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: integer\n Mode.\n\n Examples\n --------\n > var v = base.dists.bernoulli.mode( 0.1 )\n 0\n > v = base.dists.bernoulli.mode( 0.8 )\n 1\n\n","base.dists.bernoulli.pmf":"\nbase.dists.bernoulli.pmf( x, p )\n Evaluates the probability mass function (PMF) for a Bernoulli distribution\n with success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated PMF.\n\n Examples\n --------\n > var y = base.dists.bernoulli.pmf( 1.0, 0.3 )\n 0.3\n > y = base.dists.bernoulli.pmf( 0.0, 0.7 )\n 0.3\n > y = base.dists.bernoulli.pmf( -1.0, 0.5 )\n 0.0\n > y = base.dists.bernoulli.pmf( 0.0, NaN )\n NaN\n > y = base.dists.bernoulli.pmf( NaN, 0.5 )\n NaN\n // Invalid success probability:\n > y = base.dists.bernoulli.pmf( 0.0, 1.5 )\n NaN\n\n\nbase.dists.bernoulli.pmf.factory( p )\n Returns a function for evaluating the probability mass function (PMF) of a\n Bernoulli distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var mypmf = base.dists.bernoulli.pmf.factory( 0.5 );\n > var y = mypmf( 1.0 )\n 0.5\n > y = mypmf( 0.0 )\n 0.5\n\n","base.dists.bernoulli.pmf.factory":"\nbase.dists.bernoulli.pmf.factory( p )\n Returns a function for evaluating the probability mass function (PMF) of a\n Bernoulli distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var mypmf = base.dists.bernoulli.pmf.factory( 0.5 );\n > var y = mypmf( 1.0 )\n 0.5\n > y = mypmf( 0.0 )\n 0.5","base.dists.bernoulli.quantile":"\nbase.dists.bernoulli.quantile( r, p )\n Evaluates the quantile function for a Bernoulli distribution with success\n probability `p` at a probability `r`.\n\n If `r < 0` or `r > 1`, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n r: number\n Input probability.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.bernoulli.quantile( 0.8, 0.4 )\n 1\n > y = base.dists.bernoulli.quantile( 0.5, 0.4 )\n 0\n > y = base.dists.bernoulli.quantile( 0.9, 0.1 )\n 0\n\n > y = base.dists.bernoulli.quantile( -0.2, 0.1 )\n NaN\n\n > y = base.dists.bernoulli.quantile( NaN, 0.8 )\n NaN\n > y = base.dists.bernoulli.quantile( 0.4, NaN )\n NaN\n\n > y = base.dists.bernoulli.quantile( 0.5, -1.0 )\n NaN\n > y = base.dists.bernoulli.quantile( 0.5, 1.5 )\n NaN\n\n\nbase.dists.bernoulli.quantile.factory( p )\n Returns a function for evaluating the quantile function of a Bernoulli\n distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.bernoulli.quantile.factory( 0.4 );\n > var y = myquantile( 0.4 )\n 0\n > y = myquantile( 0.8 )\n 1\n > y = myquantile( 1.0 )\n 1\n\n","base.dists.bernoulli.quantile.factory":"\nbase.dists.bernoulli.quantile.factory( p )\n Returns a function for evaluating the quantile function of a Bernoulli\n distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.bernoulli.quantile.factory( 0.4 );\n > var y = myquantile( 0.4 )\n 0\n > y = myquantile( 0.8 )\n 1\n > y = myquantile( 1.0 )\n 1","base.dists.bernoulli.skewness":"\nbase.dists.bernoulli.skewness( p )\n Returns the skewness of a Bernoulli distribution with success probability\n `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.bernoulli.skewness( 0.1 )\n ~2.667\n > v = base.dists.bernoulli.skewness( 0.5 )\n 0.0\n\n","base.dists.bernoulli.stdev":"\nbase.dists.bernoulli.stdev( p )\n Returns the standard deviation of a Bernoulli distribution with success\n probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.bernoulli.stdev( 0.1 )\n ~0.3\n > v = base.dists.bernoulli.stdev( 0.5 )\n 0.5\n\n","base.dists.bernoulli.variance":"\nbase.dists.bernoulli.variance( p )\n Returns the variance of a Bernoulli distribution with success probability\n `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.bernoulli.variance( 0.1 )\n ~0.09\n > v = base.dists.bernoulli.variance( 0.5 )\n 0.25\n\n","base.dists.beta.Beta":"\nbase.dists.beta.Beta( [α, β] )\n Returns a beta distribution object.\n\n Parameters\n ----------\n α: number (optional)\n First shape parameter. Must be greater than `0`. Default: `1.0`.\n\n β: number (optional)\n Second shape parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n beta: Object\n Distribution instance.\n\n beta.alpha: number\n First shape parameter. If set, the value must be greater than `0`.\n\n beta.beta: number\n Second shape parameter. If set, the value must be greater than `0`.\n\n beta.entropy: number\n Read-only property which returns the differential entropy.\n\n beta.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n beta.mean: number\n Read-only property which returns the expected value.\n\n beta.median: number\n Read-only property which returns the median.\n\n beta.mode: number\n Read-only property which returns the mode.\n\n beta.skewness: number\n Read-only property which returns the skewness.\n\n beta.stdev: number\n Read-only property which returns the standard deviation.\n\n beta.variance: number\n Read-only property which returns the variance.\n\n beta.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n beta.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n beta.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n beta.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n beta.pdf: Function\n Evaluates the probability density function (PDF).\n\n beta.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var beta = base.dists.beta.Beta( 1.0, 1.0 );\n > beta.alpha\n 1.0\n > beta.beta\n 1.0\n > beta.entropy\n 0.0\n > beta.kurtosis\n -1.2\n > beta.mean\n 0.5\n > beta.median\n 0.5\n > beta.mode\n NaN\n > beta.skewness\n 0.0\n > beta.stdev\n ~0.289\n > beta.variance\n ~0.0833\n > beta.cdf( 0.8 )\n 0.8\n > beta.logcdf( 0.8 )\n ~-0.223\n > beta.logpdf( 1.0 )\n 0.0\n > beta.mgf( 3.14 )\n ~7.0394\n > beta.pdf( 1.0 )\n 1.0\n > beta.quantile( 0.8 )\n 0.8\n\n","base.dists.beta.cdf":"\nbase.dists.beta.cdf( x, α, β )\n Evaluates the cumulative distribution function (CDF) for a beta distribution\n with first shape parameter `α` and second shape parameter `β` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.beta.cdf( 0.5, 1.0, 1.0 )\n 0.5\n > y = base.dists.beta.cdf( 0.5, 2.0, 4.0 )\n ~0.813\n > y = base.dists.beta.cdf( 0.2, 2.0, 2.0 )\n ~0.104\n > y = base.dists.beta.cdf( 0.8, 4.0, 4.0 )\n ~0.967\n > y = base.dists.beta.cdf( -0.5, 4.0, 2.0 )\n 0.0\n > y = base.dists.beta.cdf( 1.5, 4.0, 2.0 )\n 1.0\n\n > y = base.dists.beta.cdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.beta.cdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.beta.cdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.beta.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.beta.cdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.beta.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a beta distribution with first shape parameter `α` and second shape\n parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.beta.cdf.factory( 0.5, 0.5 );\n > var y = mycdf( 0.8 )\n ~0.705\n > y = mycdf( 0.3 )\n ~0.369\n\n","base.dists.beta.cdf.factory":"\nbase.dists.beta.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a beta distribution with first shape parameter `α` and second shape\n parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.beta.cdf.factory( 0.5, 0.5 );\n > var y = mycdf( 0.8 )\n ~0.705\n > y = mycdf( 0.3 )\n ~0.369","base.dists.beta.entropy":"\nbase.dists.beta.entropy( α, β )\n Returns the differential entropy of a beta distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Differential entropy.\n\n Examples\n --------\n > var v = base.dists.beta.entropy( 1.0, 1.0 )\n 0.0\n > v = base.dists.beta.entropy( 4.0, 12.0 )\n ~-0.869\n > v = base.dists.beta.entropy( 8.0, 2.0 )\n ~-0.795\n\n > v = base.dists.beta.entropy( 1.0, -0.1 )\n NaN\n > v = base.dists.beta.entropy( -0.1, 1.0 )\n NaN\n\n > v = base.dists.beta.entropy( 2.0, NaN )\n NaN\n > v = base.dists.beta.entropy( NaN, 2.0 )\n NaN\n\n","base.dists.beta.kurtosis":"\nbase.dists.beta.kurtosis( α, β )\n Returns the excess kurtosis of a beta distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.beta.kurtosis( 1.0, 1.0 )\n -1.2\n > v = base.dists.beta.kurtosis( 4.0, 12.0 )\n ~0.082\n > v = base.dists.beta.kurtosis( 8.0, 2.0 )\n ~0.490\n\n > v = base.dists.beta.kurtosis( 1.0, -0.1 )\n NaN\n > v = base.dists.beta.kurtosis( -0.1, 1.0 )\n NaN\n\n > v = base.dists.beta.kurtosis( 2.0, NaN )\n NaN\n > v = base.dists.beta.kurtosis( NaN, 2.0 )\n NaN\n\n","base.dists.beta.logcdf":"\nbase.dists.beta.logcdf( x, α, β )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a beta distribution with first shape parameter `α` and second\n shape parameter `β` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.beta.logcdf( 0.5, 1.0, 1.0 )\n ~-0.693\n > y = base.dists.beta.logcdf( 0.5, 2.0, 4.0 )\n ~-0.208\n > y = base.dists.beta.logcdf( 0.2, 2.0, 2.0 )\n ~-2.263\n > y = base.dists.beta.logcdf( 0.8, 4.0, 4.0 )\n ~-0.034\n > y = base.dists.beta.logcdf( -0.5, 4.0, 2.0 )\n -Infinity\n > y = base.dists.beta.logcdf( 1.5, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.beta.logcdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.beta.logcdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.beta.logcdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.beta.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.beta.logcdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.beta.logcdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a beta distribution with first shape\n parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.beta.logcdf.factory( 0.5, 0.5 );\n > var y = mylogcdf( 0.8 )\n ~-0.35\n > y = mylogcdf( 0.3 )\n ~-0.997\n\n","base.dists.beta.logcdf.factory":"\nbase.dists.beta.logcdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a beta distribution with first shape\n parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.beta.logcdf.factory( 0.5, 0.5 );\n > var y = mylogcdf( 0.8 )\n ~-0.35\n > y = mylogcdf( 0.3 )\n ~-0.997","base.dists.beta.logpdf":"\nbase.dists.beta.logpdf( x, α, β )\n Evaluates the natural logarithm of the probability density function (PDF)\n for a beta distribution with first shape parameter `α` and second shape\n parameter `β` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Natural logarithm of the PDF.\n\n Examples\n --------\n > var y = base.dists.beta.logpdf( 0.5, 1.0, 1.0 )\n 0.0\n > y = base.dists.beta.logpdf( 0.5, 2.0, 4.0 )\n ~0.223\n > y = base.dists.beta.logpdf( 0.2, 2.0, 2.0 )\n ~-0.041\n > y = base.dists.beta.logpdf( 0.8, 4.0, 4.0 )\n ~-0.556\n > y = base.dists.beta.logpdf( -0.5, 4.0, 2.0 )\n -Infinity\n > y = base.dists.beta.logpdf( 1.5, 4.0, 2.0 )\n -Infinity\n\n > y = base.dists.beta.logpdf( 0.5, -1.0, 0.5 )\n NaN\n > y = base.dists.beta.logpdf( 0.5, 0.5, -1.0 )\n NaN\n\n > y = base.dists.beta.logpdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.beta.logpdf( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.beta.logpdf( 0.5, 1.0, NaN )\n NaN\n\n\nbase.dists.beta.logpdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a beta distribution with first shape parameter `α`\n and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n fcn: Function\n Function to evaluate the natural logarithm of the PDF.\n\n Examples\n --------\n > var mylogpdf = base.dists.beta.logpdf.factory( 0.5, 0.5 );\n > var y = mylogpdf( 0.8 )\n ~-0.228\n > y = mylogpdf( 0.3 )\n ~-0.364\n\n","base.dists.beta.logpdf.factory":"\nbase.dists.beta.logpdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a beta distribution with first shape parameter `α`\n and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n fcn: Function\n Function to evaluate the natural logarithm of the PDF.\n\n Examples\n --------\n > var mylogpdf = base.dists.beta.logpdf.factory( 0.5, 0.5 );\n > var y = mylogpdf( 0.8 )\n ~-0.228\n > y = mylogpdf( 0.3 )\n ~-0.364","base.dists.beta.mean":"\nbase.dists.beta.mean( α, β )\n Returns the expected value of a beta distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.beta.mean( 1.0, 1.0 )\n 0.5\n > v = base.dists.beta.mean( 4.0, 12.0 )\n 0.25\n > v = base.dists.beta.mean( 8.0, 2.0 )\n 0.8\n\n","base.dists.beta.median":"\nbase.dists.beta.median( α, β )\n Returns the median of a beta distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.beta.median( 1.0, 1.0 )\n 0.5\n > v = base.dists.beta.median( 4.0, 12.0 )\n ~0.239\n > v = base.dists.beta.median( 8.0, 2.0 )\n ~0.820\n\n > v = base.dists.beta.median( 1.0, -0.1 )\n NaN\n > v = base.dists.beta.median( -0.1, 1.0 )\n NaN\n\n > v = base.dists.beta.median( 2.0, NaN )\n NaN\n > v = base.dists.beta.median( NaN, 2.0 )\n NaN\n\n","base.dists.beta.mgf":"\nbase.dists.beta.mgf( t, α, β )\n Evaluates the moment-generating function (MGF) for a beta distribution with\n first shape parameter `α` and second shape parameter `β` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.beta.mgf( 0.5, 1.0, 1.0 )\n ~1.297\n > y = base.dists.beta.mgf( 0.5, 2.0, 4.0 )\n ~1.186\n > y = base.dists.beta.mgf( 3.0, 2.0, 2.0 )\n ~5.575\n > y = base.dists.beta.mgf( -0.8, 4.0, 4.0 )\n ~0.676\n\n > y = base.dists.beta.mgf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.beta.mgf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.beta.mgf( 0.0, 1.0, NaN )\n NaN\n\n > y = base.dists.beta.mgf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.beta.mgf( 2.0, 0.0, 0.5 )\n NaN\n\n > y = base.dists.beta.mgf( 2.0, 0.5, -1.0 )\n NaN\n > y = base.dists.beta.mgf( 2.0, 0.5, 0.0 )\n NaN\n\n\nbase.dists.beta.mgf.factory( α, β )\n Returns a function for evaluating the moment-generating function (MGF) of a\n beta distribution with first shape parameter `α` and second shape parameter\n `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.beta.mgf.factory( 0.5, 0.5 );\n > var y = myMGF( 0.8 )\n ~1.552\n > y = myMGF( 0.3 )\n ~1.168\n\n","base.dists.beta.mgf.factory":"\nbase.dists.beta.mgf.factory( α, β )\n Returns a function for evaluating the moment-generating function (MGF) of a\n beta distribution with first shape parameter `α` and second shape parameter\n `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.beta.mgf.factory( 0.5, 0.5 );\n > var y = myMGF( 0.8 )\n ~1.552\n > y = myMGF( 0.3 )\n ~1.168","base.dists.beta.mode":"\nbase.dists.beta.mode( α, β )\n Returns the mode of a beta distribution.\n\n If `α <= 1` or `β <= 1`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.beta.mode( 4.0, 12.0 )\n ~0.214\n > v = base.dists.beta.mode( 8.0, 2.0 )\n ~0.875\n > v = base.dists.beta.mode( 1.0, 1.0 )\n NaN\n\n","base.dists.beta.pdf":"\nbase.dists.beta.pdf( x, α, β )\n Evaluates the probability density function (PDF) for a beta distribution\n with first shape parameter `α` and second shape parameter `β` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.beta.pdf( 0.5, 1.0, 1.0 )\n 1.0\n > y = base.dists.beta.pdf( 0.5, 2.0, 4.0 )\n 1.25\n > y = base.dists.beta.pdf( 0.2, 2.0, 2.0 )\n ~0.96\n > y = base.dists.beta.pdf( 0.8, 4.0, 4.0 )\n ~0.573\n > y = base.dists.beta.pdf( -0.5, 4.0, 2.0 )\n 0.0\n > y = base.dists.beta.pdf( 1.5, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.beta.pdf( 0.5, -1.0, 0.5 )\n NaN\n > y = base.dists.beta.pdf( 0.5, 0.5, -1.0 )\n NaN\n\n > y = base.dists.beta.pdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.beta.pdf( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.beta.pdf( 0.5, 1.0, NaN )\n NaN\n\n\nbase.dists.beta.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF) of\n a beta distribution with first shape parameter `α` and second shape\n parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var mypdf = base.dists.beta.pdf.factory( 0.5, 0.5 );\n > var y = mypdf( 0.8 )\n ~0.796\n > y = mypdf( 0.3 )\n ~0.695\n\n","base.dists.beta.pdf.factory":"\nbase.dists.beta.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF) of\n a beta distribution with first shape parameter `α` and second shape\n parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var mypdf = base.dists.beta.pdf.factory( 0.5, 0.5 );\n > var y = mypdf( 0.8 )\n ~0.796\n > y = mypdf( 0.3 )\n ~0.695","base.dists.beta.quantile":"\nbase.dists.beta.quantile( p, α, β )\n Evaluates the quantile function for a beta distribution with first shape\n parameter `α` and second shape parameter `β` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input value (probability).\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.beta.quantile( 0.8, 2.0, 1.0 )\n ~0.894\n > y = base.dists.beta.quantile( 0.5, 4.0, 2.0 )\n ~0.686\n > y = base.dists.beta.quantile( 1.1, 1.0, 1.0 )\n NaN\n > y = base.dists.beta.quantile( -0.2, 1.0, 1.0 )\n NaN\n\n > y = base.dists.beta.quantile( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.beta.quantile( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.beta.quantile( 0.5, 1.0, NaN )\n NaN\n\n > y = base.dists.beta.quantile( 0.5, -1.0, 1.0 )\n NaN\n > y = base.dists.beta.quantile( 0.5, 1.0, -1.0 )\n NaN\n\n\nbase.dists.beta.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of a beta\n distribution with first shape parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.beta.quantile.factory( 2.0, 2.0 );\n > y = myquantile( 0.8 )\n ~0.713\n > y = myquantile( 0.4 )\n ~0.433\n\n","base.dists.beta.quantile.factory":"\nbase.dists.beta.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of a beta\n distribution with first shape parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.beta.quantile.factory( 2.0, 2.0 );\n > y = myquantile( 0.8 )\n ~0.713\n > y = myquantile( 0.4 )\n ~0.433","base.dists.beta.skewness":"\nbase.dists.beta.skewness( α, β )\n Returns the skewness of a beta distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.beta.skewness( 1.0, 1.0 )\n 0.0\n > v = base.dists.beta.skewness( 4.0, 12.0 )\n ~0.529\n > v = base.dists.beta.skewness( 8.0, 2.0 )\n ~-0.829\n\n > v = base.dists.beta.skewness( 1.0, -0.1 )\n NaN\n > v = base.dists.beta.skewness( -0.1, 1.0 )\n NaN\n\n > v = base.dists.beta.skewness( 2.0, NaN )\n NaN\n > v = base.dists.beta.skewness( NaN, 2.0 )\n NaN\n\n","base.dists.beta.stdev":"\nbase.dists.beta.stdev( α, β )\n Returns the standard deviation of a beta distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.beta.stdev( 1.0, 1.0 )\n ~0.289\n > v = base.dists.beta.stdev( 4.0, 12.0 )\n ~0.105\n > v = base.dists.beta.stdev( 8.0, 2.0 )\n ~0.121\n\n > v = base.dists.beta.stdev( 1.0, -0.1 )\n NaN\n > v = base.dists.beta.stdev( -0.1, 1.0 )\n NaN\n\n > v = base.dists.beta.stdev( 2.0, NaN )\n NaN\n > v = base.dists.beta.stdev( NaN, 2.0 )\n NaN\n\n","base.dists.beta.variance":"\nbase.dists.beta.variance( α, β )\n Returns the variance of a beta distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.beta.variance( 1.0, 1.0 )\n ~0.083\n > v = base.dists.beta.variance( 4.0, 12.0 )\n ~0.011\n > v = base.dists.beta.variance( 8.0, 2.0 )\n ~0.015\n\n > v = base.dists.beta.variance( 1.0, -0.1 )\n NaN\n > v = base.dists.beta.variance( -0.1, 1.0 )\n NaN\n\n > v = base.dists.beta.variance( 2.0, NaN )\n NaN\n > v = base.dists.beta.variance( NaN, 2.0 )\n NaN\n\n","base.dists.betaprime.BetaPrime":"\nbase.dists.betaprime.BetaPrime( [α, β] )\n Returns a beta prime distribution object.\n\n Parameters\n ----------\n α: number (optional)\n First shape parameter. Must be greater than `0`. Default: `1.0`.\n\n β: number (optional)\n Second shape parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n betaprime: Object\n Distribution instance.\n\n betaprime.alpha: number\n First shape parameter. If set, the value must be greater than `0`.\n\n betaprime.beta: number\n Second shape parameter. If set, the value must be greater than `0`.\n\n betaprime.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n betaprime.mean: number\n Read-only property which returns the expected value.\n\n betaprime.mode: number\n Read-only property which returns the mode.\n\n betaprime.skewness: number\n Read-only property which returns the skewness.\n\n betaprime.stdev: number\n Read-only property which returns the standard deviation.\n\n betaprime.variance: number\n Read-only property which returns the variance.\n\n betaprime.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n betaprime.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n betaprime.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n betaprime.pdf: Function\n Evaluates the probability density function (PDF).\n\n betaprime.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var betaprime = base.dists.betaprime.BetaPrime( 6.0, 5.0 );\n > betaprime.alpha\n 6.0\n > betaprime.beta\n 5.0\n > betaprime.kurtosis\n 44.4\n > betaprime.mean\n 1.5\n > betaprime.mode\n ~0.833\n > betaprime.skewness\n ~3.578\n > betaprime.stdev\n ~1.118\n > betaprime.variance\n 1.25\n > betaprime.cdf( 0.8 )\n ~0.25\n > betaprime.logcdf( 0.8 )\n ~-1.387\n > betaprime.logpdf( 1.0 )\n ~-0.486\n > betaprime.pdf( 1.0 )\n ~0.615\n > betaprime.quantile( 0.8 )\n ~2.06\n\n","base.dists.betaprime.cdf":"\nbase.dists.betaprime.cdf( x, α, β )\n Evaluates the cumulative distribution function (CDF) for a beta prime\n distribution with first shape parameter `α` and second shape parameter `β`\n at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.betaprime.cdf( 0.5, 1.0, 1.0 )\n ~0.333\n > y = base.dists.betaprime.cdf( 0.5, 2.0, 4.0 )\n ~0.539\n > y = base.dists.betaprime.cdf( 0.2, 2.0, 2.0 )\n ~0.074\n > y = base.dists.betaprime.cdf( 0.8, 4.0, 4.0 )\n ~0.38\n > y = base.dists.betaprime.cdf( -0.5, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.betaprime.cdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.betaprime.cdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.betaprime.cdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.betaprime.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.betaprime.cdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.betaprime.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a beta prime distribution with first shape parameter `α` and second shape\n parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.betaprime.cdf.factory( 0.5, 0.5 );\n > var y = mycdf( 0.8 )\n ~0.465\n > y = mycdf( 0.3 )\n ~0.319\n\n","base.dists.betaprime.cdf.factory":"\nbase.dists.betaprime.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a beta prime distribution with first shape parameter `α` and second shape\n parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.betaprime.cdf.factory( 0.5, 0.5 );\n > var y = mycdf( 0.8 )\n ~0.465\n > y = mycdf( 0.3 )\n ~0.319","base.dists.betaprime.kurtosis":"\nbase.dists.betaprime.kurtosis( α, β )\n Returns the excess kurtosis of a beta prime distribution.\n\n If `α <= 0` or `β <= 4`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Kurtosis.\n\n Examples\n --------\n > var v = base.dists.betaprime.kurtosis( 2.0, 6.0 )\n ~26.143\n > v = base.dists.betaprime.kurtosis( 4.0, 12.0 )\n ~5.764\n > v = base.dists.betaprime.kurtosis( 8.0, 6.0 )\n ~19.962\n\n > v = base.dists.betaprime.kurtosis( 1.0, 2.8 )\n NaN\n > v = base.dists.betaprime.kurtosis( 1.0, -0.1 )\n NaN\n > v = base.dists.betaprime.kurtosis( -0.1, 5.0 )\n NaN\n\n > v = base.dists.betaprime.kurtosis( 2.0, NaN )\n NaN\n > v = base.dists.betaprime.kurtosis( NaN, 6.0 )\n NaN\n\n","base.dists.betaprime.logcdf":"\nbase.dists.betaprime.logcdf( x, α, β )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a beta prime distribution with first shape parameter `α` and\n second shape parameter `β` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.betaprime.logcdf( 0.5, 1.0, 1.0 )\n ~-1.099\n > y = base.dists.betaprime.logcdf( 0.5, 2.0, 4.0 )\n ~-0.618\n > y = base.dists.betaprime.logcdf( 0.2, 2.0, 2.0 )\n ~-2.603\n > y = base.dists.betaprime.logcdf( 0.8, 4.0, 4.0 )\n ~-0.968\n > y = base.dists.betaprime.logcdf( -0.5, 4.0, 2.0 )\n -Infinity\n\n > y = base.dists.betaprime.logcdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.betaprime.logcdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.betaprime.logcdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.betaprime.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.betaprime.logcdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.betaprime.logcdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a beta prime distribution with first shape\n parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.betaprime.logcdf.factory( 0.5, 0.5 );\n > var y = mylogcdf( 0.8 )\n ~-0.767\n > y = mylogcdf( 0.3 )\n ~-1.143\n\n","base.dists.betaprime.logcdf.factory":"\nbase.dists.betaprime.logcdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a beta prime distribution with first shape\n parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.betaprime.logcdf.factory( 0.5, 0.5 );\n > var y = mylogcdf( 0.8 )\n ~-0.767\n > y = mylogcdf( 0.3 )\n ~-1.143","base.dists.betaprime.logpdf":"\nbase.dists.betaprime.logpdf( x, α, β )\n Evaluates the natural logarithm of the probability density function (PDF)\n for a beta prime distribution with first shape parameter `α` and second\n shape parameter `β` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Natural logarithm of the PDF.\n\n Examples\n --------\n > var y = base.dists.betaprime.logpdf( 0.5, 1.0, 1.0 )\n ~-0.811\n > y = base.dists.betaprime.logpdf( 0.5, 2.0, 4.0 )\n ~-0.13\n > y = base.dists.betaprime.logpdf( 0.2, 2.0, 2.0 )\n ~-0.547\n > y = base.dists.betaprime.logpdf( 0.8, 4.0, 4.0 )\n ~-0.43\n > y = base.dists.betaprime.logpdf( -0.5, 4.0, 2.0 )\n -Infinity\n\n > y = base.dists.betaprime.logpdf( 0.5, -1.0, 0.5 )\n NaN\n > y = base.dists.betaprime.logpdf( 0.5, 0.5, -1.0 )\n NaN\n\n > y = base.dists.betaprime.logpdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.betaprime.logpdf( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.betaprime.logpdf( 0.5, 1.0, NaN )\n NaN\n\n\nbase.dists.betaprime.logpdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a beta prime distribution with first shape\n parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n fcn: Function\n Function to evaluate the natural logarithm of the PDF.\n\n Examples\n --------\n > var mylogpdf = base.dists.betaprime.logpdf.factory( 0.5, 0.5 );\n > var y = mylogpdf( 0.8 )\n ~-1.62\n > y = mylogpdf( 0.3 )\n ~-0.805\n\n","base.dists.betaprime.logpdf.factory":"\nbase.dists.betaprime.logpdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a beta prime distribution with first shape\n parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n fcn: Function\n Function to evaluate the natural logarithm of the PDF.\n\n Examples\n --------\n > var mylogpdf = base.dists.betaprime.logpdf.factory( 0.5, 0.5 );\n > var y = mylogpdf( 0.8 )\n ~-1.62\n > y = mylogpdf( 0.3 )\n ~-0.805","base.dists.betaprime.mean":"\nbase.dists.betaprime.mean( α, β )\n Returns the expected value of a beta prime distribution.\n\n If `α <= 0` or `β <= 1`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.betaprime.mean( 1.0, 2.0 )\n 1.0\n > v = base.dists.betaprime.mean( 4.0, 12.0 )\n ~0.364\n > v = base.dists.betaprime.mean( 8.0, 2.0 )\n 8.0\n\n","base.dists.betaprime.mode":"\nbase.dists.betaprime.mode( α, β )\n Returns the mode of a beta prime distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.betaprime.mode( 1.0, 2.0 )\n 0.0\n > v = base.dists.betaprime.mode( 4.0, 12.0 )\n ~0.231\n > v = base.dists.betaprime.mode( 8.0, 2.0 )\n ~2.333\n\n","base.dists.betaprime.pdf":"\nbase.dists.betaprime.pdf( x, α, β )\n Evaluates the probability density function (PDF) for a beta prime\n distribution with first shape parameter `α` and second shape parameter `β`\n at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.betaprime.pdf( 0.5, 1.0, 1.0 )\n ~0.444\n > y = base.dists.betaprime.pdf( 0.5, 2.0, 4.0 )\n ~0.878\n > y = base.dists.betaprime.pdf( 0.2, 2.0, 2.0 )\n ~0.579\n > y = base.dists.betaprime.pdf( 0.8, 4.0, 4.0 )\n ~0.65\n > y = base.dists.betaprime.pdf( -0.5, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.betaprime.pdf( 0.5, -1.0, 0.5 )\n NaN\n > y = base.dists.betaprime.pdf( 0.5, 0.5, -1.0 )\n NaN\n\n > y = base.dists.betaprime.pdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.betaprime.pdf( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.betaprime.pdf( 0.5, 1.0, NaN )\n NaN\n\n\nbase.dists.betaprime.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF) of\n a beta prime distribution with first shape parameter `α` and second shape\n parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var mypdf = base.dists.betaprime.pdf.factory( 0.5, 0.5 );\n > var y = mypdf( 0.8 )\n ~0.198\n > y = mypdf( 0.3 )\n ~0.447\n\n","base.dists.betaprime.pdf.factory":"\nbase.dists.betaprime.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF) of\n a beta prime distribution with first shape parameter `α` and second shape\n parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var mypdf = base.dists.betaprime.pdf.factory( 0.5, 0.5 );\n > var y = mypdf( 0.8 )\n ~0.198\n > y = mypdf( 0.3 )\n ~0.447","base.dists.betaprime.quantile":"\nbase.dists.betaprime.quantile( p, α, β )\n Evaluates the quantile function for a beta prime distribution with first\n shape parameter `α` and second shape parameter `β` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input value (probability).\n\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.betaprime.quantile( 0.8, 2.0, 1.0 )\n ~8.472\n > y = base.dists.betaprime.quantile( 0.5, 4.0, 2.0 )\n ~2.187\n > y = base.dists.betaprime.quantile( 1.1, 1.0, 1.0 )\n NaN\n > y = base.dists.betaprime.quantile( -0.2, 1.0, 1.0 )\n NaN\n\n > y = base.dists.betaprime.quantile( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.betaprime.quantile( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.betaprime.quantile( 0.5, 1.0, NaN )\n NaN\n\n > y = base.dists.betaprime.quantile( 0.5, -1.0, 1.0 )\n NaN\n > y = base.dists.betaprime.quantile( 0.5, 1.0, -1.0 )\n NaN\n\n\nbase.dists.betaprime.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of a beta prime\n distribution with first shape parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.betaprime.quantile.factory( 2.0, 2.0 );\n > y = myQuantile( 0.8 )\n ~2.483\n > y = myQuantile( 0.4 )\n ~0.763\n\n","base.dists.betaprime.quantile.factory":"\nbase.dists.betaprime.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of a beta prime\n distribution with first shape parameter `α` and second shape parameter `β`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.betaprime.quantile.factory( 2.0, 2.0 );\n > y = myQuantile( 0.8 )\n ~2.483\n > y = myQuantile( 0.4 )\n ~0.763","base.dists.betaprime.skewness":"\nbase.dists.betaprime.skewness( α, β )\n Returns the skewness of a beta prime distribution.\n\n If `α <= 0` or `β <= 3`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.betaprime.skewness( 2.0, 4.0 )\n ~6.261\n > v = base.dists.betaprime.skewness( 4.0, 12.0 )\n ~1.724\n > v = base.dists.betaprime.skewness( 8.0, 4.0 )\n ~5.729\n\n > v = base.dists.betaprime.skewness( 1.0, 2.8 )\n NaN\n > v = base.dists.betaprime.skewness( 1.0, -0.1 )\n NaN\n > v = base.dists.betaprime.skewness( -0.1, 4.0 )\n NaN\n\n > v = base.dists.betaprime.skewness( 2.0, NaN )\n NaN\n > v = base.dists.betaprime.skewness( NaN, 4.0 )\n NaN\n\n","base.dists.betaprime.stdev":"\nbase.dists.betaprime.stdev( α, β )\n Returns the standard deviation of a beta prime distribution.\n\n If `α <= 0` or `β <= 2`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.betaprime.stdev( 1.0, 2.5 )\n ~1.491\n > v = base.dists.betaprime.stdev( 4.0, 12.0 )\n ~0.223\n > v = base.dists.betaprime.stdev( 8.0, 2.5 )\n ~8.219\n\n > v = base.dists.betaprime.stdev( 8.0, 1.0 )\n NaN\n > v = base.dists.betaprime.stdev( 1.0, -0.1 )\n NaN\n > v = base.dists.betaprime.stdev( -0.1, 3.0 )\n NaN\n\n > v = base.dists.betaprime.stdev( 2.0, NaN )\n NaN\n > v = base.dists.betaprime.stdev( NaN, 3.0 )\n NaN\n\n","base.dists.betaprime.variance":"\nbase.dists.betaprime.variance( α, β )\n Returns the variance of a beta prime distribution.\n\n If `α <= 0` or `β <= 2`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n First shape parameter.\n\n β: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.betaprime.variance( 1.0, 2.5 )\n ~2.222\n > v = base.dists.betaprime.variance( 4.0, 12.0 )\n ~0.05\n > v = base.dists.betaprime.variance( 8.0, 2.5 )\n ~67.556\n\n > v = base.dists.betaprime.variance( 8.0, 1.0 )\n NaN\n > v = base.dists.betaprime.variance( 1.0, -0.1 )\n NaN\n > v = base.dists.betaprime.variance( -0.1, 3.0 )\n NaN\n\n > v = base.dists.betaprime.variance( 2.0, NaN )\n NaN\n > v = base.dists.betaprime.variance( NaN, 3.0 )\n NaN\n\n","base.dists.binomial.Binomial":"\nbase.dists.binomial.Binomial( [n, p] )\n Returns a binomial distribution object.\n\n Parameters\n ----------\n n: integer (optional)\n Number of trials. Must be a positive integer. Default: `1`.\n\n p: number (optional)\n Success probability. Must be a number between `0` and `1`. Default:\n `0.5`.\n\n Returns\n -------\n binomial: Object\n Distribution instance.\n\n binomial.n: number\n Number of trials. If set, the value must be a positive integer.\n\n binomial.p: number\n Success probability. If set, the value must be a number between `0` and\n `1`.\n\n binomial.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n binomial.mean: number\n Read-only property which returns the expected value.\n\n binomial.median: number\n Read-only property which returns the median.\n\n binomial.mode: number\n Read-only property which returns the mode.\n\n binomial.skewness: number\n Read-only property which returns the skewness.\n\n binomial.stdev: number\n Read-only property which returns the standard deviation.\n\n binomial.variance: number\n Read-only property which returns the variance.\n\n binomial.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n binomial.logpmf: Function\n Evaluates the natural logarithm of the probability mass function (PMF).\n\n binomial.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n binomial.pmf: Function\n Evaluates the probability mass function (PMF).\n\n binomial.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var binomial = base.dists.binomial.Binomial( 8, 0.5 );\n > binomial.n\n 8.0\n > binomial.p\n 0.5\n > binomial.kurtosis\n -0.25\n > binomial.mean\n 4.0\n > binomial.median\n 4.0\n > binomial.mode\n 4.0\n > binomial.skewness\n 0.0\n > binomial.stdev\n ~1.414\n > binomial.variance\n 2.0\n > binomial.cdf( 2.9 )\n ~0.145\n > binomial.logpmf( 3.0 )\n ~-1.52\n > binomial.mgf( 0.2 )\n ~2.316\n > binomial.pmf( 3.0 )\n ~0.219\n > binomial.quantile( 0.8 )\n 5.0\n\n","base.dists.binomial.cdf":"\nbase.dists.binomial.cdf( x, n, p )\n Evaluates the cumulative distribution function (CDF) for a binomial\n distribution with number of trials `n` and success probability `p` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.binomial.cdf( 3.0, 20, 0.2 )\n ~0.411\n > y = base.dists.binomial.cdf( 21.0, 20, 0.2 )\n 1.0\n > y = base.dists.binomial.cdf( 5.0, 10, 0.4 )\n ~0.834\n > y = base.dists.binomial.cdf( 0.0, 10, 0.4 )\n ~0.006\n > y = base.dists.binomial.cdf( NaN, 20, 0.5 )\n NaN\n > y = base.dists.binomial.cdf( 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.binomial.cdf( 0.0, 20, NaN )\n NaN\n > y = base.dists.binomial.cdf( 2.0, 1.5, 0.5 )\n NaN\n > y = base.dists.binomial.cdf( 2.0, -2.0, 0.5 )\n NaN\n > y = base.dists.binomial.cdf( 2.0, 20, -1.0 )\n NaN\n > y = base.dists.binomial.cdf( 2.0, 20, 1.5 )\n NaN\n\n\nbase.dists.binomial.cdf.factory( n, p )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a binomial distribution with number of trials `n` and success probability\n `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.binomial.cdf.factory( 10, 0.5 );\n > var y = mycdf( 3.0 )\n ~0.172\n > y = mycdf( 1.0 )\n ~0.011\n\n","base.dists.binomial.cdf.factory":"\nbase.dists.binomial.cdf.factory( n, p )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a binomial distribution with number of trials `n` and success probability\n `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.binomial.cdf.factory( 10, 0.5 );\n > var y = mycdf( 3.0 )\n ~0.172\n > y = mycdf( 1.0 )\n ~0.011","base.dists.binomial.entropy":"\nbase.dists.binomial.entropy( n, p )\n Returns the entropy of a binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.binomial.entropy( 100, 0.1 )\n ~2.511\n > v = base.dists.binomial.entropy( 20, 0.5 )\n ~2.223\n > v = base.dists.binomial.entropy( 10.3, 0.5 )\n NaN\n > v = base.dists.binomial.entropy( 20, 1.1 )\n NaN\n > v = base.dists.binomial.entropy( 20, NaN )\n NaN\n\n","base.dists.binomial.kurtosis":"\nbase.dists.binomial.kurtosis( n, p )\n Returns the excess kurtosis of a binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.binomial.kurtosis( 100, 0.1 )\n ~0.051\n > v = base.dists.binomial.kurtosis( 20, 0.5 )\n ~-0.1\n > v = base.dists.binomial.kurtosis( 10.3, 0.5 )\n NaN\n > v = base.dists.binomial.kurtosis( 20, 1.1 )\n NaN\n > v = base.dists.binomial.kurtosis( 20, NaN )\n NaN\n\n","base.dists.binomial.logpmf":"\nbase.dists.binomial.logpmf( x, n, p )\n Evaluates the natural logarithm of the probability mass function (PMF) for a\n binomial distribution with number of trials `n` and success probability `p`\n at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated logPMF.\n\n Examples\n --------\n > var y = base.dists.binomial.logpmf( 3.0, 20, 0.2 )\n ~-1.583\n > y = base.dists.binomial.logpmf( 21.0, 20, 0.2 )\n -Infinity\n > y = base.dists.binomial.logpmf( 5.0, 10, 0.4 )\n ~-1.606\n > y = base.dists.binomial.logpmf( 0.0, 10, 0.4 )\n ~-5.108\n > y = base.dists.binomial.logpmf( NaN, 20, 0.5 )\n NaN\n > y = base.dists.binomial.logpmf( 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.binomial.logpmf( 0.0, 20, NaN )\n NaN\n > y = base.dists.binomial.logpmf( 2.0, 1.5, 0.5 )\n NaN\n > y = base.dists.binomial.logpmf( 2.0, -2.0, 0.5 )\n NaN\n > y = base.dists.binomial.logpmf( 2.0, 20, -1.0 )\n NaN\n > y = base.dists.binomial.logpmf( 2.0, 20, 1.5 )\n NaN\n\n\nbase.dists.binomial.logpmf.factory( n, p )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a binomial distribution with number of trials `n` and\n success probability `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogpmf = base.dists.binomial.logpmf.factory( 10, 0.5 );\n > var y = mylogpmf( 3.0 )\n ~-2.144\n > y = mylogpmf( 5.0 )\n ~-1.402\n\n","base.dists.binomial.logpmf.factory":"\nbase.dists.binomial.logpmf.factory( n, p )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a binomial distribution with number of trials `n` and\n success probability `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogpmf = base.dists.binomial.logpmf.factory( 10, 0.5 );\n > var y = mylogpmf( 3.0 )\n ~-2.144\n > y = mylogpmf( 5.0 )\n ~-1.402","base.dists.binomial.mean":"\nbase.dists.binomial.mean( n, p )\n Returns the expected value of a binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.binomial.mean( 100, 0.1 )\n 10.0\n > v = base.dists.binomial.mean( 20, 0.5 )\n 10.0\n > v = base.dists.binomial.mean( 10.3, 0.5 )\n NaN\n > v = base.dists.binomial.mean( 20, 1.1 )\n NaN\n > v = base.dists.binomial.mean( 20, NaN )\n NaN\n\n","base.dists.binomial.median":"\nbase.dists.binomial.median( n, p )\n Returns the median of a binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.binomial.median( 100, 0.1 )\n 10\n > v = base.dists.binomial.median( 20, 0.5 )\n 10\n > v = base.dists.binomial.median( 10.3, 0.5 )\n NaN\n > v = base.dists.binomial.median( 20, 1.1 )\n NaN\n > v = base.dists.binomial.median( 20, NaN )\n NaN\n\n","base.dists.binomial.mgf":"\nbase.dists.binomial.mgf( t, n, p )\n Evaluates the moment-generating function (MGF) for a binomial distribution\n with number of trials `n` and success probability `p` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.binomial.mgf( 0.5, 20, 0.2 )\n ~11.471\n > y = base.dists.binomial.mgf( 5.0, 20, 0.2 )\n ~4.798e+29\n > y = base.dists.binomial.mgf( 0.9, 10, 0.4 )\n ~99.338\n > y = base.dists.binomial.mgf( 0.0, 10, 0.4 )\n 1.0\n\n > y = base.dists.binomial.mgf( NaN, 20, 0.5 )\n NaN\n > y = base.dists.binomial.mgf( 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.binomial.mgf( 0.0, 20, NaN )\n NaN\n\n > y = base.dists.binomial.mgf( 2.0, 1.5, 0.5 )\n NaN\n > y = base.dists.binomial.mgf( 2.0, -2.0, 0.5 )\n NaN\n > y = base.dists.binomial.mgf( 2.0, 20, -1.0 )\n NaN\n > y = base.dists.binomial.mgf( 2.0, 20, 1.5 )\n NaN\n\n\nbase.dists.binomial.mgf.factory( n, p )\n Returns a function for evaluating the moment-generating function (MGF) of a\n binomial distribution with number of trials `n` and success probability `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.binomial.mgf.factory( 10, 0.5 );\n > var y = myMGF( 0.3 )\n ~5.013\n\n","base.dists.binomial.mgf.factory":"\nbase.dists.binomial.mgf.factory( n, p )\n Returns a function for evaluating the moment-generating function (MGF) of a\n binomial distribution with number of trials `n` and success probability `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.binomial.mgf.factory( 10, 0.5 );\n > var y = myMGF( 0.3 )\n ~5.013","base.dists.binomial.mode":"\nbase.dists.binomial.mode( n, p )\n Returns the mode of a binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.binomial.mode( 100, 0.1 )\n 10\n > v = base.dists.binomial.mode( 20, 0.5 )\n 10\n > v = base.dists.binomial.mode( 10.3, 0.5 )\n NaN\n > v = base.dists.binomial.mode( 20, 1.1 )\n NaN\n > v = base.dists.binomial.mode( 20, NaN )\n NaN\n\n","base.dists.binomial.pmf":"\nbase.dists.binomial.pmf( x, n, p )\n Evaluates the probability mass function (PMF) for a binomial distribution\n with number of trials `n` and success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated PMF.\n\n Examples\n --------\n > var y = base.dists.binomial.pmf( 3.0, 20, 0.2 )\n ~0.205\n > y = base.dists.binomial.pmf( 21.0, 20, 0.2 )\n 0.0\n > y = base.dists.binomial.pmf( 5.0, 10, 0.4 )\n ~0.201\n > y = base.dists.binomial.pmf( 0.0, 10, 0.4 )\n ~0.006\n > y = base.dists.binomial.pmf( NaN, 20, 0.5 )\n NaN\n > y = base.dists.binomial.pmf( 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.binomial.pmf( 0.0, 20, NaN )\n NaN\n > y = base.dists.binomial.pmf( 2.0, 1.5, 0.5 )\n NaN\n > y = base.dists.binomial.pmf( 2.0, -2.0, 0.5 )\n NaN\n > y = base.dists.binomial.pmf( 2.0, 20, -1.0 )\n NaN\n > y = base.dists.binomial.pmf( 2.0, 20, 1.5 )\n NaN\n\n\nbase.dists.binomial.pmf.factory( n, p )\n Returns a function for evaluating the probability mass function (PMF) of a\n binomial distribution with number of trials `n` and success probability `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var mypmf = base.dists.binomial.pmf.factory( 10, 0.5 );\n > var y = mypmf( 3.0 )\n ~0.117\n > y = mypmf( 5.0 )\n ~0.246\n\n","base.dists.binomial.pmf.factory":"\nbase.dists.binomial.pmf.factory( n, p )\n Returns a function for evaluating the probability mass function (PMF) of a\n binomial distribution with number of trials `n` and success probability `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var mypmf = base.dists.binomial.pmf.factory( 10, 0.5 );\n > var y = mypmf( 3.0 )\n ~0.117\n > y = mypmf( 5.0 )\n ~0.246","base.dists.binomial.quantile":"\nbase.dists.binomial.quantile( r, n, p )\n Evaluates the quantile function for a binomial distribution with number of\n trials `n` and success probability `p` at a probability `r`.\n\n If `r < 0` or `r > 1`, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n r: number\n Input probability.\n\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.binomial.quantile( 0.4, 20, 0.2 )\n 3\n > y = base.dists.binomial.quantile( 0.8, 20, 0.2 )\n 5\n > y = base.dists.binomial.quantile( 0.5, 10, 0.4 )\n 4\n > y = base.dists.binomial.quantile( 0.0, 10, 0.4 )\n 0\n > y = base.dists.binomial.quantile( 1.0, 10, 0.4 )\n 10\n\n > y = base.dists.binomial.quantile( NaN, 20, 0.5 )\n NaN\n > y = base.dists.binomial.quantile( 0.2, NaN, 0.5 )\n NaN\n > y = base.dists.binomial.quantile( 0.2, 20, NaN )\n NaN\n\n > y = base.dists.binomial.quantile( 0.5, 1.5, 0.5 )\n NaN\n > y = base.dists.binomial.quantile( 0.5, -2.0, 0.5 )\n NaN\n\n > y = base.dists.binomial.quantile( 0.5, 20, -1.0 )\n NaN\n > y = base.dists.binomial.quantile( 0.5, 20, 1.5 )\n NaN\n\n\nbase.dists.binomial.quantile.factory( n, p )\n Returns a function for evaluating the quantile function of a binomial\n distribution with number of trials `n` and success probability `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.binomial.quantile.factory( 10, 0.5 );\n > var y = myquantile( 0.1 )\n 3\n > y = myquantile( 0.9 )\n 7\n\n","base.dists.binomial.quantile.factory":"\nbase.dists.binomial.quantile.factory( n, p )\n Returns a function for evaluating the quantile function of a binomial\n distribution with number of trials `n` and success probability `p`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.binomial.quantile.factory( 10, 0.5 );\n > var y = myquantile( 0.1 )\n 3\n > y = myquantile( 0.9 )\n 7","base.dists.binomial.skewness":"\nbase.dists.binomial.skewness( n, p )\n Returns the skewness of a binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.binomial.skewness( 100, 0.1 )\n ~0.267\n > v = base.dists.binomial.skewness( 20, 0.5 )\n 0.0\n > v = base.dists.binomial.skewness( 10.3, 0.5 )\n NaN\n > v = base.dists.binomial.skewness( 20, 1.1 )\n NaN\n > v = base.dists.binomial.skewness( 20, NaN )\n NaN\n\n","base.dists.binomial.stdev":"\nbase.dists.binomial.stdev( n, p )\n Returns the standard deviation of a binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.binomial.stdev( 100, 0.1 )\n 3.0\n > v = base.dists.binomial.stdev( 20, 0.5 )\n ~2.236\n > v = base.dists.binomial.stdev( 10.3, 0.5 )\n NaN\n > v = base.dists.binomial.stdev( 20, 1.1 )\n NaN\n > v = base.dists.binomial.stdev( 20, NaN )\n NaN\n\n","base.dists.binomial.variance":"\nbase.dists.binomial.variance( n, p )\n Returns the variance of a binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a number of trials `n` which is not a nonnegative integer, the\n function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Number of trials.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.binomial.variance( 100, 0.1 )\n 9\n > v = base.dists.binomial.variance( 20, 0.5 )\n 5\n > v = base.dists.binomial.variance( 10.3, 0.5 )\n NaN\n > v = base.dists.binomial.variance( 20, 1.1 )\n NaN\n > v = base.dists.binomial.variance( 20, NaN )\n NaN\n\n","base.dists.cauchy.Cauchy":"\nbase.dists.cauchy.Cauchy( [x0, Ɣ] )\n Returns a Cauchy distribution object.\n\n Parameters\n ----------\n x0: number (optional)\n Location parameter. Default: `0.0`.\n\n Ɣ: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n cauchy: Object\n Distribution instance.\n\n cauchy.x0: number\n Location parameter.\n\n cauchy.gamma: number\n Scale parameter. If set, the value must be greater than `0`.\n\n cauchy.entropy: number\n Read-only property which returns the differential entropy.\n\n cauchy.median: number\n Read-only property which returns the median.\n\n cauchy.mode: number\n Read-only property which returns the mode.\n\n cauchy.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n cauchy.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n cauchy.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n cauchy.pdf: Function\n Evaluates the probability density function (PDF).\n\n cauchy.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var cauchy = base.dists.cauchy.Cauchy( 0.0, 1.0 );\n > cauchy.x0\n 0.0\n > cauchy.gamma\n 1.0\n > cauchy.entropy\n ~2.531\n > cauchy.median\n 0.0\n > cauchy.mode\n 0.0\n > cauchy.cdf( 0.8 )\n ~0.715\n > cauchy.logcdf( 1.0 )\n ~-0.288\n > cauchy.logpdf( 1.0 )\n ~-1.838\n > cauchy.pdf( 1.0 )\n ~0.159\n > cauchy.quantile( 0.8 )\n ~1.376\n\n","base.dists.cauchy.cdf":"\nbase.dists.cauchy.cdf( x, x0, Ɣ )\n Evaluates the cumulative distribution function (CDF) for a Cauchy\n distribution with location parameter `x0` and scale parameter `Ɣ` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `Ɣ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.cauchy.cdf( 4.0, 0.0, 2.0 )\n ~0.852\n > y = base.dists.cauchy.cdf( 1.0, 0.0, 2.0 )\n ~0.648\n > y = base.dists.cauchy.cdf( 1.0, 3.0, 2.0 )\n 0.25\n > y = base.dists.cauchy.cdf( NaN, 0.0, 2.0 )\n NaN\n > y = base.dists.cauchy.cdf( 1.0, 2.0, NaN )\n NaN\n > y = base.dists.cauchy.cdf( 1.0, NaN, 3.0 )\n NaN\n\n\nbase.dists.cauchy.cdf.factory( x0, Ɣ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Cauchy distribution with location parameter `x0` and scale parameter\n `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.cauchy.cdf.factory( 1.5, 3.0 );\n > var y = myCDF( 1.0 )\n ~0.447\n\n","base.dists.cauchy.cdf.factory":"\nbase.dists.cauchy.cdf.factory( x0, Ɣ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Cauchy distribution with location parameter `x0` and scale parameter\n `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.cauchy.cdf.factory( 1.5, 3.0 );\n > var y = myCDF( 1.0 )\n ~0.447","base.dists.cauchy.entropy":"\nbase.dists.cauchy.entropy( x0, Ɣ )\n Returns the differential entropy of a Cauchy distribution (in nats).\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `Ɣ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.cauchy.entropy( 10.0, 7.0 )\n ~4.477\n > v = base.dists.cauchy.entropy( 22.0, 0.5 )\n ~1.838\n > v = base.dists.cauchy.entropy( 10.3, -0.5 )\n NaN\n\n","base.dists.cauchy.logcdf":"\nbase.dists.cauchy.logcdf( x, x0, Ɣ )\n Evaluates the natural logarithm of the cumulative distribution function\n (logCDF) for a Cauchy distribution with location parameter `x0` and scale\n parameter `Ɣ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `Ɣ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Natural logarithm of the CDF.\n\n Examples\n --------\n > var y = base.dists.cauchy.logcdf( 4.0, 0.0, 2.0 )\n ~-0.16\n > y = base.dists.cauchy.logcdf( 1.0, 0.0, 2.0 )\n ~-0.435\n > y = base.dists.cauchy.logcdf( 1.0, 3.0, 2.0 )\n ~-1.386\n > y = base.dists.cauchy.logcdf( NaN, 0.0, 2.0 )\n NaN\n > y = base.dists.cauchy.logcdf( 1.0, 2.0, NaN )\n NaN\n > y = base.dists.cauchy.logcdf( 1.0, NaN, 3.0 )\n NaN\n\n\nbase.dists.cauchy.logcdf.factory( x0, Ɣ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (logCDF) of a Cauchy distribution with location\n parameter `x0` and scale parameter `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Function to evaluate the natural logarithm of CDF.\n\n Examples\n --------\n > var mylogCDF = base.dists.cauchy.logcdf.factory( 1.5, 3.0 );\n > var y = mylogCDF( 1.0 )\n ~-0.804\n\n","base.dists.cauchy.logcdf.factory":"\nbase.dists.cauchy.logcdf.factory( x0, Ɣ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (logCDF) of a Cauchy distribution with location\n parameter `x0` and scale parameter `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Function to evaluate the natural logarithm of CDF.\n\n Examples\n --------\n > var mylogCDF = base.dists.cauchy.logcdf.factory( 1.5, 3.0 );\n > var y = mylogCDF( 1.0 )\n ~-0.804","base.dists.cauchy.logpdf":"\nbase.dists.cauchy.logpdf( x, x0, Ɣ )\n Evaluates the natural logarithm of the probability density function (logPDF)\n for a Cauchy distribution with location parameter `x0` and scale parameter\n `Ɣ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `Ɣ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Natural logarithm of PDF.\n\n Examples\n --------\n > var y = base.dists.cauchy.logpdf( 2.0, 1.0, 1.0 )\n ~-1.838\n > y = base.dists.cauchy.logpdf( 4.0, 3.0, 0.1 )\n ~-3.457\n > y = base.dists.cauchy.logpdf( 4.0, 3.0, 3.0 )\n ~-2.349\n > y = base.dists.cauchy.logpdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.cauchy.logpdf( 2.0, NaN, 1.0 )\n NaN\n > y = base.dists.cauchy.logpdf( 2.0, 1.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.cauchy.logpdf( 2.0, 1.0, -2.0 )\n NaN\n\n\nbase.dists.cauchy.logpdf.factory( x0, Ɣ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (logPDF) of a Cauchy distribution with location parameter\n `x0` and scale parameter `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Function to evaluate the natural logarithm of the PDF.\n\n Examples\n --------\n > var mylogPDF = base.dists.cauchy.logpdf.factory( 10.0, 2.0 );\n > var y = mylogPDF( 10.0 )\n ~-1.838\n\n","base.dists.cauchy.logpdf.factory":"\nbase.dists.cauchy.logpdf.factory( x0, Ɣ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (logPDF) of a Cauchy distribution with location parameter\n `x0` and scale parameter `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Function to evaluate the natural logarithm of the PDF.\n\n Examples\n --------\n > var mylogPDF = base.dists.cauchy.logpdf.factory( 10.0, 2.0 );\n > var y = mylogPDF( 10.0 )\n ~-1.838","base.dists.cauchy.median":"\nbase.dists.cauchy.median( x0, Ɣ )\n Returns the median of a Cauchy distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `Ɣ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.cauchy.median( 10.0, 5.0 )\n 10.0\n > v = base.dists.cauchy.median( 7.0, 0.5 )\n 7.0\n > v = base.dists.cauchy.median( 10.3, -0.5 )\n NaN\n\n","base.dists.cauchy.mode":"\nbase.dists.cauchy.mode( x0, Ɣ )\n Returns the mode of a Cauchy distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `Ɣ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.cauchy.mode( 10.0, 5.0 )\n 10.0\n > v = base.dists.cauchy.mode( 7.0, 0.5 )\n 7.0\n > v = base.dists.cauchy.mode( 10.3, -0.5 )\n NaN\n\n","base.dists.cauchy.pdf":"\nbase.dists.cauchy.pdf( x, x0, Ɣ )\n Evaluates the probability density function (PDF) for a Cauchy distribution\n with location parameter `x0` and scale parameter `Ɣ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `Ɣ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.cauchy.pdf( 2.0, 1.0, 1.0 )\n ~0.159\n > y = base.dists.cauchy.pdf( 4.0, 3.0, 0.1 )\n ~0.0315\n > y = base.dists.cauchy.pdf( 4.0, 3.0, 3.0 )\n ~0.095\n > y = base.dists.cauchy.pdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.cauchy.pdf( 2.0, NaN, 1.0 )\n NaN\n > y = base.dists.cauchy.pdf( 2.0, 1.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.cauchy.pdf( 2.0, 1.0, -2.0 )\n NaN\n\n\nbase.dists.cauchy.pdf.factory( x0, Ɣ )\n Returns a function for evaluating the probability density function (PDF) of\n a Cauchy distribution with location parameter `x0` and scale parameter `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.cauchy.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n ~0.159\n\n","base.dists.cauchy.pdf.factory":"\nbase.dists.cauchy.pdf.factory( x0, Ɣ )\n Returns a function for evaluating the probability density function (PDF) of\n a Cauchy distribution with location parameter `x0` and scale parameter `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.cauchy.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n ~0.159","base.dists.cauchy.quantile":"\nbase.dists.cauchy.quantile( p, x0, Ɣ )\n Evaluates the quantile function for a Cauchy distribution with location\n parameter `x0` and scale parameter `Ɣ` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `Ɣ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.cauchy.quantile( 0.3, 2.0, 2.0 )\n ~0.547\n > y = base.dists.cauchy.quantile( 0.8, 10, 2.0 )\n ~12.753\n > y = base.dists.cauchy.quantile( 0.1, 10.0, 2.0 )\n ~3.845\n\n > y = base.dists.cauchy.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.cauchy.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.cauchy.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.cauchy.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.cauchy.quantile( 0.0, 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.cauchy.quantile( 0.5, 0.0, -1.0 )\n NaN\n\n\nbase.dists.cauchy.quantile.factory( x0, Ɣ )\n Returns a function for evaluating the quantile function of a Cauchy\n distribution with location parameter `x0` and scale parameter `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.cauchy.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0\n\n","base.dists.cauchy.quantile.factory":"\nbase.dists.cauchy.quantile.factory( x0, Ɣ )\n Returns a function for evaluating the quantile function of a Cauchy\n distribution with location parameter `x0` and scale parameter `Ɣ`.\n\n Parameters\n ----------\n x0: number\n Location parameter.\n\n Ɣ: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.cauchy.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0","base.dists.chi.cdf":"\nbase.dists.chi.cdf( x, k )\n Evaluates the cumulative distribution function (CDF) for a chi distribution\n with degrees of freedom `k` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.chi.cdf( 2.0, 3.0 )\n ~0.739\n > y = base.dists.chi.cdf( 1.0, 0.5 )\n ~0.846\n > y = base.dists.chi.cdf( -1.0, 4.0 )\n 0.0\n > y = base.dists.chi.cdf( NaN, 1.0 )\n NaN\n > y = base.dists.chi.cdf( 0.0, NaN )\n NaN\n\n // Negative degrees of freedom:\n > y = base.dists.chi.cdf( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution when `k = 0`:\n > y = base.dists.chi.cdf( 2.0, 0.0 )\n 1.0\n > y = base.dists.chi.cdf( -2.0, 0.0 )\n 0.0\n > y = base.dists.chi.cdf( 0.0, 0.0 )\n 0.0\n\nbase.dists.chi.cdf.factory( k )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a chi distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.chi.cdf.factory( 1.0 );\n > var y = mycdf( 2.0 )\n ~0.954\n > y = mycdf( 1.2 )\n ~0.77\n\n","base.dists.chi.cdf.factory":"\nbase.dists.chi.cdf.factory( k )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a chi distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.chi.cdf.factory( 1.0 );\n > var y = mycdf( 2.0 )\n ~0.954\n > y = mycdf( 1.2 )\n ~0.77","base.dists.chi.Chi":"\nbase.dists.chi.Chi( [k] )\n Returns a chi distribution object.\n\n Parameters\n ----------\n k: number (optional)\n Degrees of freedom. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n chi: Object\n Distribution instance.\n\n chi.k: number\n Degrees of freedom. If set, the value must be greater than `0`.\n\n chi.entropy: number\n Read-only property which returns the differential entropy.\n\n chi.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n chi.mean: number\n Read-only property which returns the expected value.\n\n chi.mode: number\n Read-only property which returns the mode.\n\n chi.skewness: number\n Read-only property which returns the skewness.\n\n chi.stdev: number\n Read-only property which returns the standard deviation.\n\n chi.variance: number\n Read-only property which returns the variance.\n\n chi.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n chi.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n chi.pdf: Function\n Evaluates the probability density function (PDF).\n\n chi.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var chi = base.dists.chi.Chi( 6.0 );\n > chi.k\n 6.0\n > chi.entropy\n ~1.04\n > chi.kurtosis\n ~0.025\n > chi.mean\n ~2.35\n > chi.mode\n ~2.236\n > chi.skewness\n ~0.318\n > chi.stdev\n ~0.691\n > chi.variance\n ~0.478\n > chi.cdf( 1.0 )\n ~0.014\n > chi.logpdf( 1.5 )\n ~-1.177\n > chi.pdf( 1.5 )\n ~0.308\n > chi.quantile( 0.5 )\n ~2.313\n\n","base.dists.chi.entropy":"\nbase.dists.chi.entropy( k )\n Returns the differential entropy of a chi distribution (in nats).\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.chi.entropy( 11.0 )\n ~1.056\n > v = base.dists.chi.entropy( 1.5 )\n ~0.878\n\n","base.dists.chi.kurtosis":"\nbase.dists.chi.kurtosis( k )\n Returns the excess kurtosis of a chi distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.chi.kurtosis( 9.0 )\n ~0.011\n > v = base.dists.chi.kurtosis( 1.5 )\n ~0.424\n\n","base.dists.chi.logpdf":"\nbase.dists.chi.logpdf( x, k )\n Evaluates the natural logarithm of the probability density function (PDF)\n for a chi distribution with degrees of freedom `k` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.chi.logpdf( 0.3, 4.0 )\n ~-4.35\n > y = base.dists.chi.logpdf( 0.7, 0.7 )\n ~-0.622\n > y = base.dists.chi.logpdf( -1.0, 0.5 )\n -Infinity\n > y = base.dists.chi.logpdf( 0.0, NaN )\n NaN\n > y = base.dists.chi.logpdf( NaN, 2.0 )\n NaN\n\n // Negative degrees of freedom:\n > y = base.dists.chi.logpdf( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution when `k = 0`:\n > y = base.dists.chi.logpdf( 2.0, 0.0, 2.0 )\n -Infinity\n > y = base.dists.chi.logpdf( 0.0, 0.0, 2.0 )\n Infinity\n\n\nbase.dists.chi.logpdf.factory( k )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a chi distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.chi.logpdf.factory( 6.0 );\n > var y = mylogPDF( 3.0 )\n ~-1.086\n\n","base.dists.chi.logpdf.factory":"\nbase.dists.chi.logpdf.factory( k )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a chi distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.chi.logpdf.factory( 6.0 );\n > var y = mylogPDF( 3.0 )\n ~-1.086","base.dists.chi.mean":"\nbase.dists.chi.mean( k )\n Returns the expected value of a chi distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.chi.mean( 11.0 )\n ~3.242\n > v = base.dists.chi.mean( 4.5 )\n ~2.008\n\n","base.dists.chi.mode":"\nbase.dists.chi.mode( k )\n Returns the mode of a chi distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 1`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.chi.mode( 11.0 )\n ~3.162\n > v = base.dists.chi.mode( 1.5 )\n ~0.707\n\n","base.dists.chi.pdf":"\nbase.dists.chi.pdf( x, k )\n Evaluates the probability density function (PDF) for a chi distribution with\n degrees of freedom `k` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.chi.pdf( 0.3, 4.0 )\n ~0.013\n > y = base.dists.chi.pdf( 0.7, 0.7 )\n ~0.537\n > y = base.dists.chi.pdf( -1.0, 0.5 )\n 0.0\n > y = base.dists.chi.pdf( 0.0, NaN )\n NaN\n > y = base.dists.chi.pdf( NaN, 2.0 )\n NaN\n\n // Negative degrees of freedom:\n > y = base.dists.chi.pdf( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution when `k = 0`:\n > y = base.dists.chi.pdf( 2.0, 0.0, 2.0 )\n 0.0\n > y = base.dists.chi.pdf( 0.0, 0.0, 2.0 )\n Infinity\n\n\nbase.dists.chi.pdf.factory( k )\n Returns a function for evaluating the probability density function (PDF) of\n a chi distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.chi.pdf.factory( 6.0 );\n > var y = myPDF( 3.0 )\n ~0.337\n\n","base.dists.chi.pdf.factory":"\nbase.dists.chi.pdf.factory( k )\n Returns a function for evaluating the probability density function (PDF) of\n a chi distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.chi.pdf.factory( 6.0 );\n > var y = myPDF( 3.0 )\n ~0.337","base.dists.chi.quantile":"\nbase.dists.chi.quantile( p, k )\n Evaluates the quantile function for a chi distribution with degrees of\n freedom `k` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.chi.quantile( 0.8, 1.0 )\n ~1.282\n > y = base.dists.chi.quantile( 0.5, 4.0 )\n ~1.832\n > y = base.dists.chi.quantile( 0.8, 0.1 )\n ~0.116\n > y = base.dists.chi.quantile( -0.2, 0.5 )\n NaN\n > y = base.dists.chi.quantile( 1.1, 0.5 )\n NaN\n > y = base.dists.chi.quantile( NaN, 1.0 )\n NaN\n > y = base.dists.chi.quantile( 0.0, NaN )\n NaN\n\n // Negative degrees of freedom:\n > y = base.dists.chi.quantile( 0.5, -1.0 )\n NaN\n\n // Degenerate distribution when `k = 0`:\n > y = base.dists.chi.quantile( 0.3, 0.0 )\n 0.0\n > y = base.dists.chi.quantile( 0.9, 0.0 )\n 0.0\n\n\nbase.dists.chi.quantile.factory( k )\n Returns a function for evaluating the quantile function of a chi\n distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.chi.quantile.factory( 2.0 );\n > var y = myquantile( 0.3 )\n ~0.845\n > y = myquantile( 0.7 )\n ~1.552\n\n","base.dists.chi.quantile.factory":"\nbase.dists.chi.quantile.factory( k )\n Returns a function for evaluating the quantile function of a chi\n distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.chi.quantile.factory( 2.0 );\n > var y = myquantile( 0.3 )\n ~0.845\n > y = myquantile( 0.7 )\n ~1.552","base.dists.chi.skewness":"\nbase.dists.chi.skewness( k )\n Returns the skewness of a chi distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.chi.skewness( 11.0 )\n ~0.225\n > v = base.dists.chi.skewness( 1.5 )\n ~0.763\n\n","base.dists.chi.stdev":"\nbase.dists.chi.stdev( k )\n Returns the standard deviation of a chi distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.chi.stdev( 11.0 )\n ~0.699\n > v = base.dists.chi.stdev( 1.5 )\n ~0.637\n\n","base.dists.chi.variance":"\nbase.dists.chi.variance( k )\n Returns the variance of a chi distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.chi.variance( 11.0 )\n ~0.488\n > v = base.dists.chi.variance( 1.5 )\n ~0.406\n\n","base.dists.chisquare.cdf":"\nbase.dists.chisquare.cdf( x, k )\n Evaluates the cumulative distribution function (CDF) for a chi-squared\n distribution with degrees of freedom `k` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.chisquare.cdf( 2.0, 3.0 )\n ~0.428\n > y = base.dists.chisquare.cdf( 1.0, 0.5 )\n ~0.846\n > y = base.dists.chisquare.cdf( -1.0, 4.0 )\n 0.0\n > y = base.dists.chisquare.cdf( NaN, 1.0 )\n NaN\n > y = base.dists.chisquare.cdf( 0.0, NaN )\n NaN\n\n // Negative degrees of freedom:\n > y = base.dists.chisquare.cdf( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution when `k = 0`:\n > y = base.dists.chisquare.cdf( 2.0, 0.0 )\n 1.0\n > y = base.dists.chisquare.cdf( -2.0, 0.0 )\n 0.0\n > y = base.dists.chisquare.cdf( 0.0, 0.0 )\n 0.0\n\nbase.dists.chisquare.cdf.factory( k )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a chi-squared distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.chisquare.cdf.factory( 1.0 );\n > var y = mycdf( 2.0 )\n ~0.843\n > y = mycdf( 1.2 )\n ~0.727\n\n","base.dists.chisquare.cdf.factory":"\nbase.dists.chisquare.cdf.factory( k )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a chi-squared distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.chisquare.cdf.factory( 1.0 );\n > var y = mycdf( 2.0 )\n ~0.843\n > y = mycdf( 1.2 )\n ~0.727","base.dists.chisquare.ChiSquare":"\nbase.dists.chisquare.ChiSquare( [k] )\n Returns a chi-squared distribution object.\n\n Parameters\n ----------\n k: number (optional)\n Degrees of freedom. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n chisquare: Object\n Distribution instance.\n\n chisquare.k: number\n Degrees of freedom. If set, the value must be greater than `0`.\n\n chisquare.entropy: number\n Read-only property which returns the differential entropy.\n\n chisquare.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n chisquare.mean: number\n Read-only property which returns the expected value.\n\n chisquare.median: number\n Read-only property which returns the median.\n\n chisquare.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n chisquare.mode: number\n Read-only property which returns the mode.\n\n chisquare.skewness: number\n Read-only property which returns the skewness.\n\n chisquare.stdev: number\n Read-only property which returns the standard deviation.\n\n chisquare.variance: number\n Read-only property which returns the variance.\n\n chisquare.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n chisquare.pdf: Function\n Evaluates the probability density function (PDF).\n\n chisquare.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var chisquare = base.dists.chisquare.ChiSquare( 6.0 );\n > chisquare.k\n 6.0\n > chisquare.entropy\n ~2.541\n > chisquare.kurtosis\n 2.0\n > chisquare.mean\n 6.0\n > chisquare.median\n ~5.348\n > chisquare.mode\n 4.0\n > chisquare.skewness\n ~1.155\n > chisquare.stdev\n ~3.464\n > chisquare.variance\n 12.0\n > chisquare.cdf( 3.0 )\n ~0.191\n > chisquare.mgf( 0.2 )\n ~4.63\n > chisquare.pdf( 1.5 )\n ~0.066\n > chisquare.quantile( 0.5 )\n ~5.348\n\n","base.dists.chisquare.entropy":"\nbase.dists.chisquare.entropy( k )\n Returns the differential entropy of a chi-squared distribution (in nats).\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.chisquare.entropy( 11.0 )\n ~2.901\n > v = base.dists.chisquare.entropy( 1.5 )\n ~1.375\n\n","base.dists.chisquare.kurtosis":"\nbase.dists.chisquare.kurtosis( k )\n Returns the excess kurtosis of a chi-squared distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.chisquare.kurtosis( 9.0 )\n ~1.333\n > v = base.dists.chisquare.kurtosis( 1.5 )\n 8.0\n\n","base.dists.chisquare.logpdf":"\nbase.dists.chisquare.logpdf( x, k )\n Evaluates the natural logarithm of the probability density function (PDF)\n for a chi-squared distribution with degrees of freedom `k` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.chisquare.logpdf( 0.3, 4.0 )\n ~-2.74\n > y = base.dists.chisquare.logpdf( 0.7, 0.7 )\n ~-1.295\n > y = base.dists.chisquare.logpdf( -1.0, 0.5 )\n -Infinity\n > y = base.dists.chisquare.logpdf( 0.0, NaN )\n NaN\n > y = base.dists.chisquare.logpdf( NaN, 2.0 )\n NaN\n\n // Negative degrees of freedom:\n > y = base.dists.chisquare.logpdf( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution when `k = 0`:\n > y = base.dists.chisquare.logpdf( 2.0, 0.0, 2.0 )\n -Infinity\n > y = base.dists.chisquare.logpdf( 0.0, 0.0, 2.0 )\n Infinity\n\n\nbase.dists.chisquare.logpdf.factory( k )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a chi-squared distribution with degrees of freedom\n `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var myLogPDF = base.dists.chisquare.logpdf.factory( 6.0 );\n > var y = myLogPDF( 3.0 )\n ~-2.075\n\n","base.dists.chisquare.logpdf.factory":"\nbase.dists.chisquare.logpdf.factory( k )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a chi-squared distribution with degrees of freedom\n `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var myLogPDF = base.dists.chisquare.logpdf.factory( 6.0 );\n > var y = myLogPDF( 3.0 )\n ~-2.075","base.dists.chisquare.mean":"\nbase.dists.chisquare.mean( k )\n Returns the expected value of a chi-squared distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.chisquare.mean( 11.0 )\n 11.0\n > v = base.dists.chisquare.mean( 4.5 )\n 4.5\n\n","base.dists.chisquare.median":"\nbase.dists.chisquare.median( k )\n Returns the median of a chi-squared distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var k = base.dists.chisquare.median( 9.0 )\n ~8.343\n > k = base.dists.chisquare.median( 2.0 )\n ~1.386\n\n","base.dists.chisquare.mgf":"\nbase.dists.chisquare.mgf( t, k )\n Evaluates the moment-generating function (MGF) for a chi-squared\n distribution with degrees of freedom `k` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.chisquare.mgf( 0.4, 2 )\n ~5.0\n > y = base.dists.chisquare.mgf( -1.0, 5.0 )\n ~0.0642\n > y = base.dists.chisquare.mgf( 0.0, 10.0 )\n 1.0\n\n\nbase.dists.chisquare.mgf.factory( k )\n Returns a function for evaluating the moment-generating function (MGF) of a\n chi-squared distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.chisquare.mgf.factory( 1.0 );\n > var y = mymgf( 0.2 )\n ~1.291\n > y = mymgf( 0.4 )\n ~2.236\n\n","base.dists.chisquare.mgf.factory":"\nbase.dists.chisquare.mgf.factory( k )\n Returns a function for evaluating the moment-generating function (MGF) of a\n chi-squared distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.chisquare.mgf.factory( 1.0 );\n > var y = mymgf( 0.2 )\n ~1.291\n > y = mymgf( 0.4 )\n ~2.236","base.dists.chisquare.mode":"\nbase.dists.chisquare.mode( k )\n Returns the mode of a chi-squared distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.chisquare.mode( 11.0 )\n 9.0\n > v = base.dists.chisquare.mode( 1.5 )\n 0.0\n\n","base.dists.chisquare.pdf":"\nbase.dists.chisquare.pdf( x, k )\n Evaluates the probability density function (PDF) for a chi-squared\n distribution with degrees of freedom `k` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.chisquare.pdf( 0.3, 4.0 )\n ~0.065\n > y = base.dists.chisquare.pdf( 0.7, 0.7 )\n ~0.274\n > y = base.dists.chisquare.pdf( -1.0, 0.5 )\n 0.0\n > y = base.dists.chisquare.pdf( 0.0, NaN )\n NaN\n > y = base.dists.chisquare.pdf( NaN, 2.0 )\n NaN\n\n // Negative degrees of freedom:\n > y = base.dists.chisquare.pdf( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution when `k = 0`:\n > y = base.dists.chisquare.pdf( 2.0, 0.0, 2.0 )\n 0.0\n > y = base.dists.chisquare.pdf( 0.0, 0.0, 2.0 )\n Infinity\n\n\nbase.dists.chisquare.pdf.factory( k )\n Returns a function for evaluating the probability density function (PDF) of\n a chi-squared distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.chisquare.pdf.factory( 6.0 );\n > var y = myPDF( 3.0 )\n ~0.126\n\n","base.dists.chisquare.pdf.factory":"\nbase.dists.chisquare.pdf.factory( k )\n Returns a function for evaluating the probability density function (PDF) of\n a chi-squared distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.chisquare.pdf.factory( 6.0 );\n > var y = myPDF( 3.0 )\n ~0.126","base.dists.chisquare.quantile":"\nbase.dists.chisquare.quantile( p, k )\n Evaluates the quantile function for a chi-squared distribution with degrees\n of freedom `k` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.chisquare.quantile( 0.8, 1.0 )\n ~1.642\n > y = base.dists.chisquare.quantile( 0.5, 4.0 )\n ~3.357\n > y = base.dists.chisquare.quantile( 0.8, 0.1 )\n ~0.014\n > y = base.dists.chisquare.quantile( -0.2, 0.5 )\n NaN\n > y = base.dists.chisquare.quantile( 1.1, 0.5 )\n NaN\n > y = base.dists.chisquare.quantile( NaN, 1.0 )\n NaN\n > y = base.dists.chisquare.quantile( 0.0, NaN )\n NaN\n\n // Negative degrees of freedom:\n > y = base.dists.chisquare.quantile( 0.5, -1.0 )\n NaN\n\n // Degenerate distribution when `k = 0`:\n > y = base.dists.chisquare.quantile( 0.3, 0.0 )\n 0.0\n > y = base.dists.chisquare.quantile( 0.9, 0.0 )\n 0.0\n\n\nbase.dists.chisquare.quantile.factory( k )\n Returns a function for evaluating the quantile function of a chi-squared\n distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.chisquare.quantile.factory( 2.0 );\n > var y = myquantile( 0.3 )\n ~0.713\n > y = myquantile( 0.7 )\n ~2.408\n\n","base.dists.chisquare.quantile.factory":"\nbase.dists.chisquare.quantile.factory( k )\n Returns a function for evaluating the quantile function of a chi-squared\n distribution with degrees of freedom `k`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.chisquare.quantile.factory( 2.0 );\n > var y = myquantile( 0.3 )\n ~0.713\n > y = myquantile( 0.7 )\n ~2.408","base.dists.chisquare.skewness":"\nbase.dists.chisquare.skewness( k )\n Returns the skewness of a chi-squared distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.chisquare.skewness( 11.0 )\n ~0.853\n > v = base.dists.chisquare.skewness( 1.5 )\n ~2.309\n\n","base.dists.chisquare.stdev":"\nbase.dists.chisquare.stdev( k )\n Returns the standard deviation of a chi-squared distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.chisquare.stdev( 11.0 )\n ~4.69\n > v = base.dists.chisquare.stdev( 1.5 )\n ~1.732\n\n","base.dists.chisquare.variance":"\nbase.dists.chisquare.variance( k )\n Returns the variance of a chi-squared distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `k < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.chisquare.variance( 11.0 )\n 22.0\n > v = base.dists.chisquare.variance( 1.5 )\n 3.0\n\n","base.dists.cosine.cdf":"\nbase.dists.cosine.cdf( x, μ, s )\n Evaluates the cumulative distribution function (CDF) for a raised cosine\n distribution with location parameter `μ` and scale parameter `s` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.cosine.cdf( 2.0, 0.0, 3.0 )\n ~0.971\n > y = base.dists.cosine.cdf( 9.0, 10.0, 3.0 )\n ~0.196\n\n > y = base.dists.cosine.cdf( 2.0, 0.0, NaN )\n NaN\n > y = base.dists.cosine.cdf( 2.0, NaN, 1.0 )\n NaN\n > y = base.dists.cosine.cdf( NaN, 0.0, 1.0 )\n NaN\n\n // Degenerate distribution centered at `μ` when `s = 0.0`:\n > y = base.dists.cosine.cdf( 2.0, 8.0, 0.0 )\n 0.0\n > y = base.dists.cosine.cdf( 8.0, 8.0, 0.0 )\n 1.0\n > y = base.dists.cosine.cdf( 10.0, 8.0, 0.0 )\n 1.0\n\n\nbase.dists.cosine.cdf.factory( μ, s )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a raised cosine distribution with location parameter `μ` and scale\n parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.cosine.cdf.factory( 3.0, 1.5 );\n > var y = mycdf( 1.9 )\n ~0.015\n\n","base.dists.cosine.cdf.factory":"\nbase.dists.cosine.cdf.factory( μ, s )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a raised cosine distribution with location parameter `μ` and scale\n parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.cosine.cdf.factory( 3.0, 1.5 );\n > var y = mycdf( 1.9 )\n ~0.015","base.dists.cosine.Cosine":"\nbase.dists.cosine.Cosine( [μ, s] )\n Returns a raised cosine distribution object.\n\n Parameters\n ----------\n μ: number (optional)\n Location parameter. Default: `0.0`.\n\n s: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n cosine: Object\n Distribution instance.\n\n cosine.mu: number\n Location parameter.\n\n cosine.s: number\n Scale parameter. If set, the value must be greater than `0`.\n\n cosine.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n cosine.mean: number\n Read-only property which returns the expected value.\n\n cosine.median: number\n Read-only property which returns the median.\n\n cosine.mode: number\n Read-only property which returns the mode.\n\n cosine.skewness: number\n Read-only property which returns the skewness.\n\n cosine.stdev: number\n Read-only property which returns the standard deviation.\n\n cosine.variance: number\n Read-only property which returns the variance.\n\n cosine.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n cosine.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n cosine.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n cosine.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n cosine.pdf: Function\n Evaluates the probability density function (PDF).\n\n cosine.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var cosine = base.dists.cosine.Cosine( -2.0, 3.0 );\n > cosine.mu\n -2.0\n > cosine.s\n 3.0\n > cosine.kurtosis\n ~-0.594\n > cosine.mean\n -2.0\n > cosine.median\n -2.0\n > cosine.mode\n -2.0\n > cosine.skewness\n 0.0\n > cosine.stdev\n ~1.085\n > cosine.variance\n ~1.176\n > cosine.cdf( 0.5 )\n ~0.996\n > cosine.logcdf( 0.5 )\n ~-0.004\n > cosine.logpdf( -1.0 )\n ~-1.386\n > cosine.mgf( 0.2 )\n ~0.686\n > cosine.pdf( -2.0 )\n ~0.333\n > cosine.quantile( 0.9 )\n ~-0.553\n\n","base.dists.cosine.kurtosis":"\nbase.dists.cosine.kurtosis( μ, s )\n Returns the excess kurtosis of a raised cosine distribution with location\n parameter `μ` and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var y = base.dists.cosine.kurtosis( 0.0, 1.0 )\n ~-0.594\n > y = base.dists.cosine.kurtosis( 4.0, 2.0 )\n ~-0.594\n > y = base.dists.cosine.kurtosis( NaN, 1.0 )\n NaN\n > y = base.dists.cosine.kurtosis( 0.0, NaN )\n NaN\n > y = base.dists.cosine.kurtosis( 0.0, 0.0 )\n NaN\n\n","base.dists.cosine.logcdf":"\nbase.dists.cosine.logcdf( x, μ, s )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a raised cosine distribution with location parameter `μ` and scale\n parameter `s` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.cosine.logcdf( 2.0, 0.0, 3.0 )\n ~-0.029\n > y = base.dists.cosine.logcdf( 9.0, 10.0, 3.0 )\n ~-1.632\n\n > y = base.dists.cosine.logcdf( 2.0, 0.0, NaN )\n NaN\n > y = base.dists.cosine.logcdf( 2.0, NaN, 1.0 )\n NaN\n > y = base.dists.cosine.logcdf( NaN, 0.0, 1.0 )\n NaN\n\n // Degenerate distribution centered at `μ` when `s = 0.0`:\n > y = base.dists.cosine.logcdf( 2.0, 8.0, 0.0 )\n -Infinity\n > y = base.dists.cosine.logcdf( 8.0, 8.0, 0.0 )\n 0.0\n > y = base.dists.cosine.logcdf( 10.0, 8.0, 0.0 )\n 0.0\n\n\nbase.dists.cosine.logcdf.factory( μ, s )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a raised cosine distribution with location\n parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.cosine.logcdf.factory( 3.0, 1.5 );\n > var y = mylogcdf( 1.9 )\n ~-4.2\n\n","base.dists.cosine.logcdf.factory":"\nbase.dists.cosine.logcdf.factory( μ, s )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a raised cosine distribution with location\n parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.cosine.logcdf.factory( 3.0, 1.5 );\n > var y = mylogcdf( 1.9 )\n ~-4.2","base.dists.cosine.logpdf":"\nbase.dists.cosine.logpdf( x, μ, s )\n Evaluates the logarithm of the probability density function (PDF) for a\n raised cosine distribution with location parameter `μ` and scale parameter\n `s` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.cosine.logpdf( 2.0, 0.0, 3.0 )\n ~-2.485\n > y = base.dists.cosine.logpdf( -1.0, 2.0, 4.0 )\n ~-3.307\n > y = base.dists.cosine.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.cosine.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.cosine.logpdf( 0.0, 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.cosine.logpdf( 2.0, 0.0, -1.0 )\n NaN\n\n // Degenerate distribution at `s = 0.0`:\n > y = base.dists.cosine.logpdf( 2.0, 8.0, 0.0 )\n -Infinity\n > y = base.dists.cosine.logpdf( 8.0, 8.0, 0.0 )\n Infinity\n\n\nbase.dists.cosine.logpdf.factory( μ, s )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a raised cosine distribution with location parameter `μ`\n and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.cosine.logpdf.factory( 10.0, 2.0 );\n > var y = mylogpdf( 10.0 )\n ~-0.693\n\n","base.dists.cosine.logpdf.factory":"\nbase.dists.cosine.logpdf.factory( μ, s )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a raised cosine distribution with location parameter `μ`\n and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.cosine.logpdf.factory( 10.0, 2.0 );\n > var y = mylogpdf( 10.0 )\n ~-0.693","base.dists.cosine.mean":"\nbase.dists.cosine.mean( μ, s )\n Returns the expected value of a raised cosine distribution with location\n parameter `μ` and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var y = base.dists.cosine.mean( 0.0, 1.0 )\n 0.0\n > y = base.dists.cosine.mean( 4.0, 2.0 )\n 4.0\n > y = base.dists.cosine.mean( NaN, 1.0 )\n NaN\n > y = base.dists.cosine.mean( 0.0, NaN )\n NaN\n > y = base.dists.cosine.mean( 0.0, 0.0 )\n NaN\n\n","base.dists.cosine.median":"\nbase.dists.cosine.median( μ, s )\n Returns the median of a raised cosine distribution with location parameter\n `μ` and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var y = base.dists.cosine.median( 0.0, 1.0 )\n 0.0\n > y = base.dists.cosine.median( 4.0, 2.0 )\n 4.0\n > y = base.dists.cosine.median( NaN, 1.0 )\n NaN\n > y = base.dists.cosine.median( 0.0, NaN )\n NaN\n > y = base.dists.cosine.median( 0.0, 0.0 )\n NaN\n\n","base.dists.cosine.mgf":"\nbase.dists.cosine.mgf( t, μ, s )\n Evaluates the moment-generating function (MGF) for a raised cosine\n distribution with location parameter `μ` and scale parameter `s` at a value\n `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.cosine.mgf( 2.0, 0.0, 3.0 )\n ~7.234\n > y = base.dists.cosine.mgf( 9.0, 10.0, 3.0 )\n ~1.606e+47\n\n > y = base.dists.cosine.mgf( 0.5, 0.0, NaN )\n NaN\n > y = base.dists.cosine.mgf( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.cosine.mgf( NaN, 0.0, 1.0 )\n NaN\n\n\nbase.dists.cosine.mgf.factory( μ, s )\n Returns a function for evaluating the moment-generating function (MGF) of a\n raised cosine distribution with location parameter `μ` and scale parameter\n `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.cosine.mgf.factory( 3.0, 1.5 );\n > var y = mymgf( 1.9 )\n ~495.57\n\n","base.dists.cosine.mgf.factory":"\nbase.dists.cosine.mgf.factory( μ, s )\n Returns a function for evaluating the moment-generating function (MGF) of a\n raised cosine distribution with location parameter `μ` and scale parameter\n `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.cosine.mgf.factory( 3.0, 1.5 );\n > var y = mymgf( 1.9 )\n ~495.57","base.dists.cosine.mode":"\nbase.dists.cosine.mode( μ, s )\n Returns the mode of a raised cosine distribution with location parameter `μ`\n and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var y = base.dists.cosine.mode( 0.0, 1.0 )\n 0.0\n > y = base.dists.cosine.mode( 4.0, 2.0 )\n 4.0\n > y = base.dists.cosine.mode( NaN, 1.0 )\n NaN\n > y = base.dists.cosine.mode( 0.0, NaN )\n NaN\n > y = base.dists.cosine.mode( 0.0, 0.0 )\n NaN\n\n","base.dists.cosine.pdf":"\nbase.dists.cosine.pdf( x, μ, s )\n Evaluates the probability density function (PDF) for a raised cosine\n distribution with location parameter `μ` and scale parameter `s` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.cosine.pdf( 2.0, 0.0, 3.0 )\n ~0.083\n > y = base.dists.cosine.pdf( 2.4, 4.0, 2.0 )\n ~0.048\n > y = base.dists.cosine.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.cosine.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.cosine.pdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.cosine.pdf( 2.0, 0.0, -1.0 )\n NaN\n > y = base.dists.cosine.pdf( 2.0, 8.0, 0.0 )\n 0.0\n > y = base.dists.cosine.pdf( 8.0, 8.0, 0.0 )\n Infinity\n\n\nbase.dists.cosine.pdf.factory( μ, s )\n Returns a function for evaluating the probability density function (PDF) of\n a raised cosine distribution with location parameter `μ` and scale parameter\n `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.cosine.pdf.factory( 0.0, 3.0 );\n > var y = myPDF( 2.0 )\n ~0.083\n\n","base.dists.cosine.pdf.factory":"\nbase.dists.cosine.pdf.factory( μ, s )\n Returns a function for evaluating the probability density function (PDF) of\n a raised cosine distribution with location parameter `μ` and scale parameter\n `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.cosine.pdf.factory( 0.0, 3.0 );\n > var y = myPDF( 2.0 )\n ~0.083","base.dists.cosine.quantile":"\nbase.dists.cosine.quantile( p, μ, s )\n Evaluates the quantile function for a raised cosine distribution with\n location parameter `μ` and scale parameter `s` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.cosine.quantile( 0.8, 0.0, 1.0 )\n ~0.327\n > y = base.dists.cosine.quantile( 0.5, 4.0, 2.0 )\n ~4.0\n\n > y = base.dists.cosine.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.cosine.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.cosine.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.cosine.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.cosine.quantile( 0.0, 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.cosine.quantile( 0.5, 0.0, -1.0 )\n NaN\n\n\nbase.dists.cosine.quantile.factory( μ, s )\n Returns a function for evaluating the quantile function of a raised cosine\n distribution with location parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.cosine.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.3 )\n ~9.586\n\n","base.dists.cosine.quantile.factory":"\nbase.dists.cosine.quantile.factory( μ, s )\n Returns a function for evaluating the quantile function of a raised cosine\n distribution with location parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.cosine.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.3 )\n ~9.586","base.dists.cosine.skewness":"\nbase.dists.cosine.skewness( μ, s )\n Returns the skewness of a raised cosine distribution with location parameter\n `μ` and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var y = base.dists.cosine.skewness( 0.0, 1.0 )\n 0.0\n > y = base.dists.cosine.skewness( 4.0, 2.0 )\n 0.0\n > y = base.dists.cosine.skewness( NaN, 1.0 )\n NaN\n > y = base.dists.cosine.skewness( 0.0, NaN )\n NaN\n > y = base.dists.cosine.skewness( 0.0, 0.0 )\n NaN\n\n","base.dists.cosine.stdev":"\nbase.dists.cosine.stdev( μ, s )\n Returns the standard deviation of a raised cosine distribution with location\n parameter `μ` and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var y = base.dists.cosine.stdev( 0.0, 1.0 )\n ~0.362\n > y = base.dists.cosine.stdev( 4.0, 2.0 )\n ~0.723\n > y = base.dists.cosine.stdev( NaN, 1.0 )\n NaN\n > y = base.dists.cosine.stdev( 0.0, NaN )\n NaN\n > y = base.dists.cosine.stdev( 0.0, 0.0 )\n NaN\n\n","base.dists.cosine.variance":"\nbase.dists.cosine.variance( μ, s )\n Returns the variance of a raised cosine distribution with location parameter\n `μ` and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var y = base.dists.cosine.variance( 0.0, 1.0 )\n ~0.131\n > y = base.dists.cosine.variance( 4.0, 2.0 )\n ~0.523\n > y = base.dists.cosine.variance( NaN, 1.0 )\n NaN\n > y = base.dists.cosine.variance( 0.0, NaN )\n NaN\n > y = base.dists.cosine.variance( 0.0, 0.0 )\n NaN\n\n","base.dists.degenerate.cdf":"\nbase.dists.degenerate.cdf( x, μ )\n Evaluates the cumulative distribution function (CDF) for a degenerate\n distribution with mean value `μ`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.degenerate.cdf( 2.0, 3.0 )\n 0.0\n > y = base.dists.degenerate.cdf( 4.0, 3.0 )\n 1.0\n > y = base.dists.degenerate.cdf( 3.0, 3.0 )\n 1.0\n > y = base.dists.degenerate.cdf( NaN, 0.0 )\n NaN\n > y = base.dists.degenerate.cdf( 0.0, NaN )\n NaN\n\n\nbase.dists.degenerate.cdf.factory( μ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a degenerate distribution centered at a provided mean value.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.degenerate.cdf.factory( 5.0 );\n > var y = myCDF( 3.0 )\n 0.0\n > y = myCDF( 6.0 )\n 1.0\n\n","base.dists.degenerate.cdf.factory":"\nbase.dists.degenerate.cdf.factory( μ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a degenerate distribution centered at a provided mean value.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.degenerate.cdf.factory( 5.0 );\n > var y = myCDF( 3.0 )\n 0.0\n > y = myCDF( 6.0 )\n 1.0","base.dists.degenerate.Degenerate":"\nbase.dists.degenerate.Degenerate( [μ] )\n Returns a degenerate distribution object.\n\n Parameters\n ----------\n μ: number (optional)\n Constant value of distribution.\n\n Returns\n -------\n degenerate: Object\n Distribution instance.\n\n degenerate.mu: number\n Constant value of distribution.\n\n degenerate.entropy: number\n Read-only property which returns the differential entropy.\n\n degenerate.mean: number\n Read-only property which returns the expected value.\n\n degenerate.median: number\n Read-only property which returns the median.\n\n degenerate.stdev: number\n Read-only property which returns the standard deviation.\n\n degenerate.variance: number\n Read-only property which returns the variance.\n\n degenerate.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n degenerate.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n degenerate.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n degenerate.logpmf: Function\n Evaluates the natural logarithm of the probability mass function\n (PMF).\n\n degenerate.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n degenerate.pmf: Function\n Evaluates the probability mass function (PMF).\n\n degenerate.pdf: Function\n Evaluates the probability density function (PDF).\n\n degenerate.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var degenerate = base.dists.degenerate.Degenerate( 2.0 );\n > degenerate.mu\n 2.0\n > degenerate.entropy\n 0.0\n > degenerate.mean\n 2.0\n > degenerate.mode\n 2.0\n > degenerate.median\n 2.0\n > degenerate.stdev\n 0.0\n > degenerate.variance\n 0.0\n > degenerate.cdf( 0.5 )\n 0.0\n > degenerate.logcdf( 2.5 )\n 0.0\n > degenerate.logpdf( 0.5 )\n -Infinity\n > degenerate.logpmf( 2.5 )\n -Infinity\n > degenerate.mgf( 0.2 )\n ~1.492\n > degenerate.pdf( 2.0 )\n +Infinity\n > degenerate.pmf( 2.0 )\n 1.0\n > degenerate.quantile( 0.7 )\n 2.0\n\n","base.dists.degenerate.entropy":"\nbase.dists.degenerate.entropy( μ )\n Returns the entropy of a degenerate distribution with constant value `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.degenerate.entropy( 20.0 )\n 0.0\n > v = base.dists.degenerate.entropy( -10.0 )\n 0.0\n\n","base.dists.degenerate.logcdf":"\nbase.dists.degenerate.logcdf( x, μ )\n Evaluates the natural logarithm of the cumulative distribution function\n (logCDF) for a degenerate distribution with mean `μ`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Natural logarithm of the CDF.\n\n Examples\n --------\n > var y = base.dists.degenerate.logcdf( 2.0, 3.0 )\n -Infinity\n > y = base.dists.degenerate.logcdf( 4.0, 3.0 )\n 0\n > y = base.dists.degenerate.logcdf( 3.0, 3.0 )\n 0\n > y = base.dists.degenerate.logcdf( NaN, 0.0 )\n NaN\n > y = base.dists.degenerate.logcdf( 0.0, NaN )\n NaN\n\n\nbase.dists.degenerate.logcdf.factory( μ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (logCDF) of a degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n logcdf: Function\n Function to evaluate the natural logarithm of cumulative distribution\n function (logCDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.degenerate.logcdf.factory( 5.0 );\n > var y = mylogcdf( 3.0 )\n -Infinity\n > y = mylogcdf( 6.0 )\n 0\n\n","base.dists.degenerate.logcdf.factory":"\nbase.dists.degenerate.logcdf.factory( μ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (logCDF) of a degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n logcdf: Function\n Function to evaluate the natural logarithm of cumulative distribution\n function (logCDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.degenerate.logcdf.factory( 5.0 );\n > var y = mylogcdf( 3.0 )\n -Infinity\n > y = mylogcdf( 6.0 )\n 0","base.dists.degenerate.logpdf":"\nbase.dists.degenerate.logpdf( x, μ )\n Evaluates the natural logarithm of the probability density function (logPDF)\n for a degenerate distribution with mean `μ`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Natural logarithm of the PDF.\n\n Examples\n --------\n > var y = base.dists.degenerate.logpdf( 2.0, 3.0 )\n -Infinity\n > y = base.dists.degenerate.logpdf( 3.0, 3.0 )\n Infinity\n > y = base.dists.degenerate.logpdf( NaN, 0.0 )\n NaN\n > y = base.dists.degenerate.logpdf( 0.0, NaN )\n NaN\n\n\nbase.dists.degenerate.logpdf.factory( μ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (logPDF) of a degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n logpdf: Function\n Function to evaluate the natural logarithm of the PDF.\n\n Examples\n --------\n > var mylogPDF = base.dists.degenerate.logpdf.factory( 10.0 );\n > var y = mylogPDF( 10.0 )\n Infinity\n\n","base.dists.degenerate.logpdf.factory":"\nbase.dists.degenerate.logpdf.factory( μ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (logPDF) of a degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n logpdf: Function\n Function to evaluate the natural logarithm of the PDF.\n\n Examples\n --------\n > var mylogPDF = base.dists.degenerate.logpdf.factory( 10.0 );\n > var y = mylogPDF( 10.0 )\n Infinity","base.dists.degenerate.logpmf":"\nbase.dists.degenerate.logpmf( x, μ )\n Evaluates the natural logarithm of the probability mass function (PMF) for a\n degenerate distribution with mean `μ`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Evaluated logPMF.\n\n Examples\n --------\n > var y = base.dists.degenerate.logpmf( 2.0, 3.0 )\n -Infinity\n > y = base.dists.degenerate.logpmf( 3.0, 3.0 )\n 0.0\n > y = base.dists.degenerate.logpmf( NaN, 0.0 )\n NaN\n > y = base.dists.degenerate.logpmf( 0.0, NaN )\n NaN\n\n\nbase.dists.degenerate.logpmf.factory( μ )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogPMF = base.dists.degenerate.logpmf.factory( 10.0 );\n > var y = mylogPMF( 10.0 )\n 0.0\n\n","base.dists.degenerate.logpmf.factory":"\nbase.dists.degenerate.logpmf.factory( μ )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogPMF = base.dists.degenerate.logpmf.factory( 10.0 );\n > var y = mylogPMF( 10.0 )\n 0.0","base.dists.degenerate.mean":"\nbase.dists.degenerate.mean( μ )\n Returns the expected value of a degenerate distribution with constant value\n `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.degenerate.mean( 20.0 )\n 20.0\n > v = base.dists.degenerate.mean( -10.0 )\n -10.0\n\n","base.dists.degenerate.median":"\nbase.dists.degenerate.median( μ )\n Returns the median of a degenerate distribution with constant value `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.degenerate.median( 20.0 )\n 20.0\n > v = base.dists.degenerate.median( -10.0 )\n -10.0\n\n","base.dists.degenerate.mgf":"\nbase.dists.degenerate.mgf( x, μ )\n Evaluates the moment-generating function (MGF) for a degenerate distribution\n with mean `μ`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.degenerate.mgf( 1.0, 1.0 )\n ~2.718\n > y = base.dists.degenerate.mgf( 2.0, 3.0 )\n ~403.429\n > y = base.dists.degenerate.mgf( NaN, 0.0 )\n NaN\n > y = base.dists.degenerate.mgf( 0.0, NaN )\n NaN\n\n\nbase.dists.degenerate.mgf.factory( μ )\n Returns a function for evaluating the moment-generating function (MGF) of a\n degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.degenerate.mgf.factory( 10.0 );\n > var y = myMGF( 0.1 )\n ~2.718\n\n","base.dists.degenerate.mgf.factory":"\nbase.dists.degenerate.mgf.factory( μ )\n Returns a function for evaluating the moment-generating function (MGF) of a\n degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.degenerate.mgf.factory( 10.0 );\n > var y = myMGF( 0.1 )\n ~2.718","base.dists.degenerate.mode":"\nbase.dists.degenerate.mode( μ )\n Returns the mode of a degenerate distribution with constant value `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.degenerate.mode( 20.0 )\n 20.0\n > v = base.dists.degenerate.mode( -10.0 )\n -10.0\n\n","base.dists.degenerate.pdf":"\nbase.dists.degenerate.pdf( x, μ )\n Evaluates the probability density function (PDF) for a degenerate\n distribution with mean `μ`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.degenerate.pdf( 2.0, 3.0 )\n 0.0\n > y = base.dists.degenerate.pdf( 3.0, 3.0 )\n Infinity\n > y = base.dists.degenerate.pdf( NaN, 0.0 )\n NaN\n > y = base.dists.degenerate.pdf( 0.0, NaN )\n NaN\n\n\nbase.dists.degenerate.pdf.factory( μ )\n Returns a function for evaluating the probability density function (PDF) of\n a degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.degenerate.pdf.factory( 10.0 );\n > var y = myPDF( 10.0 )\n Infinity\n\n","base.dists.degenerate.pdf.factory":"\nbase.dists.degenerate.pdf.factory( μ )\n Returns a function for evaluating the probability density function (PDF) of\n a degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.degenerate.pdf.factory( 10.0 );\n > var y = myPDF( 10.0 )\n Infinity","base.dists.degenerate.pmf":"\nbase.dists.degenerate.pmf( x, μ )\n Evaluates the probability mass function (PMF) for a degenerate distribution\n with mean `μ`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Evaluated PMF.\n\n Examples\n --------\n > var y = base.dists.degenerate.pmf( 2.0, 3.0 )\n 0.0\n > y = base.dists.degenerate.pmf( 3.0, 3.0 )\n 1.0\n > y = base.dists.degenerate.pmf( NaN, 0.0 )\n NaN\n > y = base.dists.degenerate.pmf( 0.0, NaN )\n NaN\n\n\nbase.dists.degenerate.pmf.factory( μ )\n Returns a function for evaluating the probability mass function (PMF) of a\n degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var myPMF = base.dists.degenerate.pmf.factory( 10.0 );\n > var y = myPMF( 10.0 )\n 1.0\n\n","base.dists.degenerate.pmf.factory":"\nbase.dists.degenerate.pmf.factory( μ )\n Returns a function for evaluating the probability mass function (PMF) of a\n degenerate distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var myPMF = base.dists.degenerate.pmf.factory( 10.0 );\n > var y = myPMF( 10.0 )\n 1.0","base.dists.degenerate.quantile":"\nbase.dists.degenerate.quantile( p, μ )\n Evaluates the quantile function for a degenerate distribution with mean `μ`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` for any argument, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.degenerate.quantile( 0.5, 2.0 )\n 2.0\n > y = base.dists.degenerate.quantile( 0.9, 4.0 )\n 4.0\n > y = base.dists.degenerate.quantile( 1.1, 0.0 )\n NaN\n > y = base.dists.degenerate.quantile( -0.2, 0.0 )\n NaN\n > y = base.dists.degenerate.quantile( NaN, 0.0 )\n NaN\n > y = base.dists.degenerate.quantile( 0.0, NaN )\n NaN\n\n\nbase.dists.degenerate.quantile.factory( μ )\n Returns a function for evaluating the quantile function of a degenerate\n distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.degenerate.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0\n\n","base.dists.degenerate.quantile.factory":"\nbase.dists.degenerate.quantile.factory( μ )\n Returns a function for evaluating the quantile function of a degenerate\n distribution with mean `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.degenerate.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0","base.dists.degenerate.stdev":"\nbase.dists.degenerate.stdev( μ )\n Returns the standard deviation of a degenerate distribution with constant\n value `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.degenerate.stdev( 20.0 )\n 0.0\n > v = base.dists.degenerate.stdev( -10.0 )\n 0.0\n\n","base.dists.degenerate.variance":"\nbase.dists.degenerate.variance( μ )\n Returns the variance of a degenerate distribution with constant value `μ`.\n\n Parameters\n ----------\n μ: number\n Constant value of distribution.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.degenerate.variance( 20.0 )\n 0.0\n > v = base.dists.degenerate.variance( -10.0 )\n 0.0\n\n","base.dists.discreteUniform.cdf":"\nbase.dists.discreteUniform.cdf( x, a, b )\n Evaluates the cumulative distribution function (CDF) for a discrete uniform\n distribution with minimum support `a` and maximum support `b` at a value\n `x`.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.discreteUniform.cdf( 9.0, 0, 10 )\n ~0.909\n > y = base.dists.discreteUniform.cdf( 0.5, 0, 2 )\n ~0.333\n > y = base.dists.discreteUniform.cdf( PINF, 2, 4 )\n 1.0\n > y = base.dists.discreteUniform.cdf( NINF, 2, 4 )\n 0.0\n > y = base.dists.discreteUniform.cdf( NaN, 0, 1 )\n NaN\n > y = base.dists.discreteUniform.cdf( 0.0, NaN, 1 )\n NaN\n > y = base.dists.discreteUniform.cdf( 0.0, 0, NaN )\n NaN\n > y = base.dists.discreteUniform.cdf( 2.0, 1, 0 )\n NaN\n\n\nbase.dists.discreteUniform.cdf.factory( a, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a discrete uniform distribution with minimum support `a` and maximum\n support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.discreteUniform.cdf.factory( 0, 10 );\n > var y = mycdf( 0.5 )\n ~0.091\n > y = mycdf( 8.0 )\n ~0.818\n\n","base.dists.discreteUniform.cdf.factory":"\nbase.dists.discreteUniform.cdf.factory( a, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a discrete uniform distribution with minimum support `a` and maximum\n support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.discreteUniform.cdf.factory( 0, 10 );\n > var y = mycdf( 0.5 )\n ~0.091\n > y = mycdf( 8.0 )\n ~0.818","base.dists.discreteUniform.DiscreteUniform":"\nbase.dists.discreteUniform.DiscreteUniform( [a, b] )\n Returns a discrete uniform distribution object.\n\n Parameters\n ----------\n a: integer (optional)\n Minimum support. Must be an integer smaller than `b`. Default: `0`.\n\n b: integer (optional)\n Maximum support. Must be an integer greater than `a`. Default: `1`.\n\n Returns\n -------\n discreteUniform: Object\n Distribution instance.\n\n discreteUniform.a: integer\n Minimum support. If set, the value must be an integer smaller than or\n equal to `b`.\n\n discreteUniform.b: integer\n Maximum support. If set, the value must be an integer greater than or\n equal to `a`.\n\n discreteUniform.entropy: number\n Read-only property which returns the entropy.\n\n discreteUniform.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n discreteUniform.mean: number\n Read-only property which returns the expected value.\n\n discreteUniform.median: number\n Read-only property which returns the median.\n\n discreteUniform.skewness: number\n Read-only property which returns the skewness.\n\n discreteUniform.stdev: number\n Read-only property which returns the standard deviation.\n\n discreteUniform.variance: number\n Read-only property which returns the variance.\n\n discreteUniform.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n discreteUniform.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n discreteUniform.logpmf: Function\n Evaluates the natural logarithm of the probability mass function (PMF).\n\n discreteUniform.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n discreteUniform.pmf: Function\n Evaluates the probability mass function (PMF).\n\n discreteUniform.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var discreteUniform = base.dists.discreteUniform.DiscreteUniform( -2, 2 );\n > discreteUniform.a\n -2\n > discreteUniform.b\n 2\n > discreteUniform.entropy\n ~1.609\n > discreteUniform.kurtosis\n -1.3\n > discreteUniform.mean\n 0.0\n > discreteUniform.median\n 0.0\n > discreteUniform.skewness\n 0.0\n > discreteUniform.stdev\n ~1.414\n > discreteUniform.variance\n 2.0\n > discreteUniform.cdf( 0.8 )\n 0.6\n > discreteUniform.logcdf( 0.5 )\n ~-0.511\n > discreteUniform.logpmf( 1.0 )\n ~-1.609\n > discreteUniform.mgf( 0.8 )\n ~1.766\n > discreteUniform.pmf( 0.0 )\n 0.2\n > discreteUniform.quantile( 0.8 )\n 2.0\n\n","base.dists.discreteUniform.entropy":"\nbase.dists.discreteUniform.entropy( a, b )\n Returns the entropy of a discrete uniform distribution.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.discreteUniform.entropy( 0, 1 )\n ~0.693\n > v = base.dists.discreteUniform.entropy( 4, 12 )\n ~2.197\n > v = base.dists.discreteUniform.entropy( 2, 8 )\n ~1.946\n\n","base.dists.discreteUniform.kurtosis":"\nbase.dists.discreteUniform.kurtosis( a, b )\n Returns the excess kurtosis of a discrete uniform distribution.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.discreteUniform.kurtosis( 0, 1 )\n -2.0\n > v = base.dists.discreteUniform.kurtosis( 4, 12 )\n ~-1.23\n > v = base.dists.discreteUniform.kurtosis( -4, 8 )\n ~-1.214\n\n","base.dists.discreteUniform.logcdf":"\nbase.dists.discreteUniform.logcdf( x, a, b )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a discrete uniform distribution with minimum support `a` and\n maximum support `b` at a value `x`.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.discreteUniform.logcdf( 9.0, 0, 10 )\n ~-0.095\n > y = base.dists.discreteUniform.logcdf( 0.5, 0, 2 )\n ~-1.099\n > y = base.dists.discreteUniform.logcdf( PINF, 2, 4 )\n 0.0\n > y = base.dists.discreteUniform.logcdf( NINF, 2, 4 )\n -Infinity\n > y = base.dists.discreteUniform.logcdf( NaN, 0, 1 )\n NaN\n > y = base.dists.discreteUniform.logcdf( 0.0, NaN, 1 )\n NaN\n > y = base.dists.discreteUniform.logcdf( 0.0, 0, NaN )\n NaN\n > y = base.dists.discreteUniform.logcdf( 2.0, 1, 0 )\n NaN\n\n\nbase.dists.discreteUniform.logcdf.factory( a, b )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a discrete uniform distribution with minimum\n support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var myLogCDF = base.dists.discreteUniform.logcdf.factory( 0, 10 );\n > var y = myLogCDF( 0.5 )\n ~-2.398\n > y = myLogCDF( 8.0 )\n ~-0.201\n\n","base.dists.discreteUniform.logcdf.factory":"\nbase.dists.discreteUniform.logcdf.factory( a, b )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a discrete uniform distribution with minimum\n support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var myLogCDF = base.dists.discreteUniform.logcdf.factory( 0, 10 );\n > var y = myLogCDF( 0.5 )\n ~-2.398\n > y = myLogCDF( 8.0 )\n ~-0.201","base.dists.discreteUniform.logpmf":"\nbase.dists.discreteUniform.logpmf( x, a, b )\n Evaluates the natural logarithm of the probability mass function (PMF) for a\n discrete uniform distribution with minimum support `a` and maximum support\n `b` at a value `x`.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated logPMF.\n\n Examples\n --------\n > var y = base.dists.discreteUniform.logpmf( 2.0, 0, 4 )\n ~-1.609\n > y = base.dists.discreteUniform.logpmf( 5.0, 0, 4 )\n -Infinity\n > y = base.dists.discreteUniform.logpmf( 3.0, -4, 4 )\n ~-2.197\n > y = base.dists.discreteUniform.logpmf( NaN, 0, 1 )\n NaN\n > y = base.dists.discreteUniform.logpmf( 0.0, NaN, 1 )\n NaN\n > y = base.dists.discreteUniform.logpmf( 0.0, 0, NaN )\n NaN\n > y = base.dists.discreteUniform.logpmf( 2.0, 3, 1 )\n NaN\n > y = base.dists.discreteUniform.logpmf( 2.0, 1, 2.4 )\n NaN\n\n\nbase.dists.discreteUniform.logpmf.factory( a, b )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a discrete uniform distribution with minimum support\n `a` and maximum support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var myLogPMF = base.dists.discreteUniform.logpmf.factory( 6, 7 );\n > var y = myLogPMF( 7.0 )\n ~-0.693\n > y = myLogPMF( 5.0 )\n -Infinity\n\n","base.dists.discreteUniform.logpmf.factory":"\nbase.dists.discreteUniform.logpmf.factory( a, b )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a discrete uniform distribution with minimum support\n `a` and maximum support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var myLogPMF = base.dists.discreteUniform.logpmf.factory( 6, 7 );\n > var y = myLogPMF( 7.0 )\n ~-0.693\n > y = myLogPMF( 5.0 )\n -Infinity","base.dists.discreteUniform.mean":"\nbase.dists.discreteUniform.mean( a, b )\n Returns the expected value of a discrete uniform distribution.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.discreteUniform.mean( -2, 2 )\n 0.0\n > v = base.dists.discreteUniform.mean( 4, 12 )\n 8.0\n > v = base.dists.discreteUniform.mean( 2, 8 )\n 5.0\n\n","base.dists.discreteUniform.median":"\nbase.dists.discreteUniform.median( a, b )\n Returns the median of a discrete uniform distribution.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.discreteUniform.median( -2, 2 )\n 0.0\n > v = base.dists.discreteUniform.median( 4, 12 )\n 8.0\n > v = base.dists.discreteUniform.median( 2, 8 )\n 5.0\n\n","base.dists.discreteUniform.mgf":"\nbase.dists.discreteUniform.mgf( t, a, b )\n Evaluates the moment-generating function (MGF) for a discrete uniform\n distribution with minimum support `a` and maximum support `b` at a value\n `t`.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.discreteUniform.mgf( 2.0, 0, 4 )\n ~689.475\n > y = base.dists.discreteUniform.mgf( -0.2, 0, 4 )\n ~0.697\n > y = base.dists.discreteUniform.mgf( 2.0, 0, 1 )\n ~4.195\n > y = base.dists.discreteUniform.mgf( 0.5, 3, 2 )\n NaN\n > y = base.dists.discreteUniform.mgf( NaN, 0, 1 )\n NaN\n > y = base.dists.discreteUniform.mgf( 0.0, NaN, 1 )\n NaN\n > y = base.dists.discreteUniform.mgf( 0.0, 0, NaN )\n NaN\n\n\nbase.dists.discreteUniform.mgf.factory( a, b )\n Returns a function for evaluating the moment-generating function (MGF)\n of a discrete uniform distribution with minimum support `a` and maximum\n support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.discreteUniform.mgf.factory( 6, 7 );\n > var y = mymgf( 0.1 )\n ~1.918\n > y = mymgf( 1.1 )\n ~1471.722\n\n","base.dists.discreteUniform.mgf.factory":"\nbase.dists.discreteUniform.mgf.factory( a, b )\n Returns a function for evaluating the moment-generating function (MGF)\n of a discrete uniform distribution with minimum support `a` and maximum\n support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.discreteUniform.mgf.factory( 6, 7 );\n > var y = mymgf( 0.1 )\n ~1.918\n > y = mymgf( 1.1 )\n ~1471.722","base.dists.discreteUniform.pmf":"\nbase.dists.discreteUniform.pmf( x, a, b )\n Evaluates the probability mass function (PMF) for a discrete uniform\n distribution with minimum support `a` and maximum support `b` at a value\n `x`.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated PMF.\n\n Examples\n --------\n > var y = base.dists.discreteUniform.pmf( 2.0, 0, 4 )\n ~0.2\n > y = base.dists.discreteUniform.pmf( 5.0, 0, 4 )\n 0.0\n > y = base.dists.discreteUniform.pmf( 3.0, -4, 4 )\n ~0.111\n > y = base.dists.discreteUniform.pmf( NaN, 0, 1 )\n NaN\n > y = base.dists.discreteUniform.pmf( 0.0, NaN, 1 )\n NaN\n > y = base.dists.discreteUniform.pmf( 0.0, 0, NaN )\n NaN\n > y = base.dists.discreteUniform.pmf( 2.0, 3, 1 )\n NaN\n > y = base.dists.discreteUniform.pmf( 2.0, 1, 2.4 )\n NaN\n\n\nbase.dists.discreteUniform.pmf.factory( a, b )\n Returns a function for evaluating the probability mass function (PMF) of\n a discrete uniform distribution with minimum support `a` and maximum support\n `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var myPMF = base.dists.discreteUniform.pmf.factory( 6, 7 );\n > var y = myPMF( 7.0 )\n 0.5\n > y = myPMF( 5.0 )\n 0.0\n\n","base.dists.discreteUniform.pmf.factory":"\nbase.dists.discreteUniform.pmf.factory( a, b )\n Returns a function for evaluating the probability mass function (PMF) of\n a discrete uniform distribution with minimum support `a` and maximum support\n `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var myPMF = base.dists.discreteUniform.pmf.factory( 6, 7 );\n > var y = myPMF( 7.0 )\n 0.5\n > y = myPMF( 5.0 )\n 0.0","base.dists.discreteUniform.quantile":"\nbase.dists.discreteUniform.quantile( p, a, b )\n Evaluates the quantile function for a discrete uniform distribution with\n minimum support `a` and maximum support `b` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n If provided `a > b`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.discreteUniform.quantile( 0.8, 0, 1 )\n 1\n > y = base.dists.discreteUniform.quantile( 0.5, 0.0, 10.0 )\n 5\n\n > y = base.dists.discreteUniform.quantile( 1.1, 0, 4 )\n NaN\n > y = base.dists.discreteUniform.quantile( -0.2, 0, 4 )\n NaN\n\n > y = base.dists.discreteUniform.quantile( NaN, -2, 2 )\n NaN\n > y = base.dists.discreteUniform.quantile( 0.1, NaN, 2 )\n NaN\n > y = base.dists.discreteUniform.quantile( 0.1, -2, NaN )\n NaN\n\n > y = base.dists.discreteUniform.quantile( 0.5, 2, 1 )\n NaN\n\n\nbase.dists.discreteUniform.quantile.factory( a, b )\n Returns a function for evaluating the quantile function of a discrete\n uniform distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.discreteUniform.quantile.factory( 0, 4 );\n > var y = myQuantile( 0.8 )\n 4\n\n","base.dists.discreteUniform.quantile.factory":"\nbase.dists.discreteUniform.quantile.factory( a, b )\n Returns a function for evaluating the quantile function of a discrete\n uniform distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.discreteUniform.quantile.factory( 0, 4 );\n > var y = myQuantile( 0.8 )\n 4","base.dists.discreteUniform.skewness":"\nbase.dists.discreteUniform.skewness( a, b )\n Returns the skewness of a discrete uniform distribution.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.discreteUniform.skewness( -2, 2 )\n 0.0\n > v = base.dists.discreteUniform.skewness( 4, 12 )\n 0.0\n > v = base.dists.discreteUniform.skewness( 2, 8 )\n 0.0\n\n","base.dists.discreteUniform.stdev":"\nbase.dists.discreteUniform.stdev( a, b )\n Returns the standard deviation of a discrete uniform distribution.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.discreteUniform.stdev( 0, 1 )\n ~0.5\n > v = base.dists.discreteUniform.stdev( 4, 12 )\n ~2.582\n > v = base.dists.discreteUniform.stdev( 2, 8 )\n 2.0\n\n","base.dists.discreteUniform.variance":"\nbase.dists.discreteUniform.variance( a, b )\n Returns the variance of a discrete uniform distribution.\n\n If `a > b`, the function returns `NaN`.\n\n If `a` or `b` is not an integer value, the function returns `NaN`.\n\n Parameters\n ----------\n a: integer\n Minimum support.\n\n b: integer\n Maximum support.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.discreteUniform.variance( 0, 1 )\n ~0.25\n > v = base.dists.discreteUniform.variance( 4, 12 )\n ~6.667\n > v = base.dists.discreteUniform.variance( 2, 8 )\n 4.0\n\n","base.dists.erlang.cdf":"\nbase.dists.erlang.cdf( x, k, λ )\n Evaluates the cumulative distribution function (CDF) for an Erlang\n distribution with shape parameter `k` and rate parameter `λ` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a nonnegative integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.erlang.cdf( 2.0, 1, 1.0 )\n ~0.865\n > y = base.dists.erlang.cdf( 2.0, 3, 1.0 )\n ~0.323\n > y = base.dists.erlang.cdf( 2.0, 2.5, 1.0 )\n NaN\n > y = base.dists.erlang.cdf( -1.0, 2, 2.0 )\n 0.0\n > y = base.dists.erlang.cdf( PINF, 4, 2.0 )\n 1.0\n > y = base.dists.erlang.cdf( NINF, 4, 2.0 )\n 0.0\n > y = base.dists.erlang.cdf( NaN, 0, 1.0 )\n NaN\n > y = base.dists.erlang.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.erlang.cdf( 0.0, 0, NaN )\n NaN\n > y = base.dists.erlang.cdf( 2.0, -1, 1.0 )\n NaN\n > y = base.dists.erlang.cdf( 2.0, 1, -1.0 )\n NaN\n\n\nbase.dists.erlang.cdf.factory( k, λ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of an Erlang distribution with shape parameter `k` and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.erlang.cdf.factory( 2, 0.5 );\n > var y = mycdf( 6.0 )\n ~0.801\n > y = mycdf( 2.0 )\n ~0.264\n\n","base.dists.erlang.cdf.factory":"\nbase.dists.erlang.cdf.factory( k, λ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of an Erlang distribution with shape parameter `k` and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.erlang.cdf.factory( 2, 0.5 );\n > var y = mycdf( 6.0 )\n ~0.801\n > y = mycdf( 2.0 )\n ~0.264","base.dists.erlang.entropy":"\nbase.dists.erlang.entropy( k, λ )\n Returns the differential entropy of an Erlang distribution (in nats).\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a positive integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n k: integer\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.erlang.entropy( 1, 1.0 )\n ~1.0\n > v = base.dists.erlang.entropy( 4, 12.0 )\n ~-0.462\n > v = base.dists.erlang.entropy( 8, 2.0 )\n ~1.723\n\n","base.dists.erlang.Erlang":"\nbase.dists.erlang.Erlang( [k, λ] )\n Returns an Erlang distribution object.\n\n Parameters\n ----------\n k: number (optional)\n Shape parameter. Must be a positive integer. Default: `1.0`.\n\n λ: number (optional)\n Rate parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n erlang: Object\n Distribution instance.\n\n erlang.k: number\n Shape parameter. If set, the value must be a positive integer.\n\n erlang.lambda: number\n Rate parameter. If set, the value must be greater than `0`.\n\n erlang.entropy: number\n Read-only property which returns the differential entropy.\n\n erlang.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n erlang.mean: number\n Read-only property which returns the expected value.\n\n erlang.mode: number\n Read-only property which returns the mode.\n\n erlang.skewness: number\n Read-only property which returns the skewness.\n\n erlang.stdev: number\n Read-only property which returns the standard deviation.\n\n erlang.variance: number\n Read-only property which returns the variance.\n\n erlang.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n erlang.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n erlang.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n erlang.pdf: Function\n Evaluates the probability density function (PDF).\n\n erlang.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var erlang = base.dists.erlang.Erlang( 6, 5.0 );\n > erlang.k\n 6\n > erlang.lambda\n 5.0\n > erlang.entropy\n ~0.647\n > erlang.kurtosis\n 1.0\n > erlang.mean\n 1.2\n > erlang.mode\n 1.0\n > erlang.skewness\n ~0.816\n > erlang.stdev\n ~0.49\n > erlang.variance\n 0.24\n > erlang.cdf( 3.0 )\n ~0.997\n > erlang.logpdf( 3.0 )\n ~-4.638\n > erlang.mgf( -0.5 )\n ~0.564\n > erlang.pdf( 3.0 )\n ~0.01\n > erlang.quantile( 0.8 )\n ~1.581\n\n","base.dists.erlang.kurtosis":"\nbase.dists.erlang.kurtosis( k, λ )\n Returns the excess kurtosis of an Erlang distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a positive integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n k: integer\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.erlang.kurtosis( 1, 1.0 )\n 6.0\n > v = base.dists.erlang.kurtosis( 4, 12.0 )\n 1.5\n > v = base.dists.erlang.kurtosis( 8, 2.0 )\n 0.75\n\n","base.dists.erlang.logpdf":"\nbase.dists.erlang.logpdf( x, k, λ )\n Evaluates the natural logarithm of the probability density function (PDF)\n for an Erlang distribution with shape parameter `k` and rate parameter `λ`\n at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a nonnegative integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.erlang.logpdf( 0.1, 1, 1.0 )\n ~-0.1\n > y = base.dists.erlang.logpdf( 0.5, 2, 2.5 )\n ~-0.111\n > y = base.dists.erlang.logpdf( -1.0, 4, 2.0 )\n -Infinity\n > y = base.dists.erlang.logpdf( NaN, 1, 1.0 )\n NaN\n > y = base.dists.erlang.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.erlang.logpdf( 0.0, 1, NaN )\n NaN\n > y = base.dists.erlang.logpdf( 2.0, -2, 0.5 )\n NaN\n > y = base.dists.erlang.logpdf( 2.0, 0.5, 0.5 )\n NaN\n > y = base.dists.erlang.logpdf( 2.0, 0.0, 2.0 )\n -Infinity\n > y = base.dists.erlang.logpdf( 0.0, 0.0, 2.0 )\n Infinity\n > y = base.dists.erlang.logpdf( 2.0, 1, 0.0 )\n NaN\n > y = base.dists.erlang.logpdf( 2.0, 1, -1.0 )\n NaN\n\n\nbase.dists.erlang.logpdf.factory( k, λ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of an Erlang distribution with shape parameter `k`\n and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var myLogPDF = base.dists.erlang.logpdf.factory( 6.0, 7.0 );\n > y = myLogPDF( 7.0 )\n ~-32.382\n\n\n","base.dists.erlang.logpdf.factory":"\nbase.dists.erlang.logpdf.factory( k, λ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of an Erlang distribution with shape parameter `k`\n and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var myLogPDF = base.dists.erlang.logpdf.factory( 6.0, 7.0 );\n > y = myLogPDF( 7.0 )\n ~-32.382","base.dists.erlang.mean":"\nbase.dists.erlang.mean( k, λ )\n Returns the expected value of an Erlang distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a positive integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n k: integer\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.erlang.mean( 1, 1.0 )\n 1.0\n > v = base.dists.erlang.mean( 4, 12.0 )\n ~0.333\n > v = base.dists.erlang.mean( 8, 2.0 )\n 4.0\n\n","base.dists.erlang.mgf":"\nbase.dists.erlang.mgf( t, k, λ )\n Evaluates the moment-generating function (MGF) for an Erlang distribution\n with shape parameter `k` and rate parameter `λ` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a nonnegative integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.erlang.mgf( 0.3, 1, 1.0 )\n ~1.429\n > y = base.dists.erlang.mgf( 2.0, 2, 3.0 )\n ~9.0\n > y = base.dists.erlang.mgf( -1.0, 2, 2.0 )\n ~0.444\n\n > y = base.dists.erlang.mgf( NaN, 1, 1.0 )\n NaN\n > y = base.dists.erlang.mgf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.erlang.mgf( 0.0, 1, NaN )\n NaN\n\n > y = base.dists.erlang.mgf( 0.2, -2, 0.5 )\n NaN\n > y = base.dists.erlang.mgf( 0.2, 0.5, 0.5 )\n NaN\n\n > y = base.dists.erlang.mgf( 0.2, 1, 0.0 )\n NaN\n > y = base.dists.erlang.mgf( 0.2, 1, -5.0 )\n NaN\n\n\nbase.dists.erlang.mgf.factory( k, λ )\n Returns a function for evaluating the moment-generating function (MGF) of an\n Erlang distribution with shape parameter `k` and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.erlang.mgf.factory( 2, 0.5 );\n > var y = myMGF( 0.2 )\n ~2.778\n > y = myMGF( -0.5 )\n 0.25\n\n","base.dists.erlang.mgf.factory":"\nbase.dists.erlang.mgf.factory( k, λ )\n Returns a function for evaluating the moment-generating function (MGF) of an\n Erlang distribution with shape parameter `k` and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.erlang.mgf.factory( 2, 0.5 );\n > var y = myMGF( 0.2 )\n ~2.778\n > y = myMGF( -0.5 )\n 0.25","base.dists.erlang.mode":"\nbase.dists.erlang.mode( k, λ )\n Returns the mode of an Erlang distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a positive integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n k: integer\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.erlang.mode( 1, 1.0 )\n 0.0\n > v = base.dists.erlang.mode( 4, 12.0 )\n 0.25\n > v = base.dists.erlang.mode( 8, 2.0 )\n 3.5\n\n","base.dists.erlang.pdf":"\nbase.dists.erlang.pdf( x, k, λ )\n Evaluates the probability density function (PDF) for an Erlang distribution\n with shape parameter `k` and rate parameter `λ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a nonnegative integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.erlang.pdf( 0.1, 1, 1.0 )\n ~0.905\n > y = base.dists.erlang.pdf( 0.5, 2, 2.5 )\n ~0.895\n > y = base.dists.erlang.pdf( -1.0, 4, 2.0 )\n 0.0\n > y = base.dists.erlang.pdf( NaN, 1, 1.0 )\n NaN\n > y = base.dists.erlang.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.erlang.pdf( 0.0, 1, NaN )\n NaN\n > y = base.dists.erlang.pdf( 2.0, -2, 0.5 )\n NaN\n > y = base.dists.erlang.pdf( 2.0, 0.5, 0.5 )\n NaN\n > y = base.dists.erlang.pdf( 2.0, 0.0, 2.0 )\n 0.0\n > y = base.dists.erlang.pdf( 0.0, 0.0, 2.0 )\n Infinity\n > y = base.dists.erlang.pdf( 2.0, 1, 0.0 )\n NaN\n > y = base.dists.erlang.pdf( 2.0, 1, -1.0 )\n NaN\n\n\nbase.dists.erlang.pdf.factory( k, λ )\n Returns a function for evaluating the probability density function (PDF)\n of an Erlang distribution with shape parameter `k` and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.erlang.pdf.factory( 6.0, 7.0 );\n > y = myPDF( 7.0 )\n ~8.639e-15\n\n\n","base.dists.erlang.pdf.factory":"\nbase.dists.erlang.pdf.factory( k, λ )\n Returns a function for evaluating the probability density function (PDF)\n of an Erlang distribution with shape parameter `k` and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.erlang.pdf.factory( 6.0, 7.0 );\n > y = myPDF( 7.0 )\n ~8.639e-15","base.dists.erlang.quantile":"\nbase.dists.erlang.quantile( p, k, λ )\n Evaluates the quantile function for an Erlang distribution with shape\n parameter `k` and rate parameter `λ` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a nonnegative integer for `k`, the function returns `NaN`.\n\n If provided a non-positive number for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.erlang.quantile( 0.8, 2, 1.0 )\n ~2.994\n > y = base.dists.erlang.quantile( 0.5, 4, 2.0 )\n ~1.836\n\n > y = base.dists.erlang.quantile( 1.1, 1, 1.0 )\n NaN\n > y = base.dists.erlang.quantile( -0.2, 1, 1.0 )\n NaN\n\n > y = base.dists.erlang.quantile( NaN, 1, 1.0 )\n NaN\n > y = base.dists.erlang.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.erlang.quantile( 0.0, 1, NaN )\n NaN\n\n // Non-integer shape parameter:\n > y = base.dists.erlang.quantile( 0.5, 0.5, 1.0 )\n NaN\n // Non-positive shape parameter:\n > y = base.dists.erlang.quantile( 0.5, -1, 1.0 )\n NaN\n // Non-positive rate parameter:\n > y = base.dists.erlang.quantile( 0.5, 1, -1.0 )\n NaN\n\n\nbase.dists.erlang.quantile.factory( k, λ )\n Returns a function for evaluating the quantile function of an Erlang\n distribution with shape parameter `k` and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.erlang.quantile.factory( 10, 2.0 );\n > var y = myQuantile( 0.4 )\n ~4.452\n\n","base.dists.erlang.quantile.factory":"\nbase.dists.erlang.quantile.factory( k, λ )\n Returns a function for evaluating the quantile function of an Erlang\n distribution with shape parameter `k` and rate parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.erlang.quantile.factory( 10, 2.0 );\n > var y = myQuantile( 0.4 )\n ~4.452","base.dists.erlang.skewness":"\nbase.dists.erlang.skewness( k, λ )\n Returns the skewness of an Erlang distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a positive integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n k: integer\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.erlang.skewness( 1, 1.0 )\n 2.0\n > v = base.dists.erlang.skewness( 4, 12.0 )\n 1.0\n > v = base.dists.erlang.skewness( 8, 2.0 )\n ~0.707\n\n","base.dists.erlang.stdev":"\nbase.dists.erlang.stdev( k, λ )\n Returns the standard deviation of an Erlang distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a positive integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n k: integer\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.erlang.stdev( 1, 1.0 )\n 1.0\n > v = base.dists.erlang.stdev( 4, 12.0 )\n ~0.167\n > v = base.dists.erlang.stdev( 8, 2.0 )\n ~1.414\n\n","base.dists.erlang.variance":"\nbase.dists.erlang.variance( k, λ )\n Returns the variance of an Erlang distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a positive integer for `k`, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n k: integer\n Shape parameter.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.erlang.variance( 1, 1.0 )\n 1.0\n > v = base.dists.erlang.variance( 4, 12.0 )\n ~0.028\n > v = base.dists.erlang.variance( 8, 2.0 )\n 2.0\n\n","base.dists.exponential.cdf":"\nbase.dists.exponential.cdf( x, λ )\n Evaluates the cumulative distribution function (CDF) for an exponential\n distribution with rate parameter `λ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.exponential.cdf( 2.0, 0.1 )\n ~0.181\n > y = base.dists.exponential.cdf( 1.0, 2.0 )\n ~0.865\n > y = base.dists.exponential.cdf( -1.0, 4.0 )\n 0.0\n > y = base.dists.exponential.cdf( NaN, 1.0 )\n NaN\n > y = base.dists.exponential.cdf( 0.0, NaN )\n NaN\n\n // Negative rate parameter:\n > y = base.dists.exponential.cdf( 2.0, -1.0 )\n NaN\n\nbase.dists.exponential.cdf.factory( λ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n for an exponential distribution with rate parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.exponential.cdf.factory( 0.5 );\n > var y = myCDF( 3.0 )\n ~0.777\n\n","base.dists.exponential.cdf.factory":"\nbase.dists.exponential.cdf.factory( λ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n for an exponential distribution with rate parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.exponential.cdf.factory( 0.5 );\n > var y = myCDF( 3.0 )\n ~0.777","base.dists.exponential.entropy":"\nbase.dists.exponential.entropy( λ )\n Returns the differential entropy of an exponential distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.exponential.entropy( 11.0 )\n ~-1.398\n > v = base.dists.exponential.entropy( 4.5 )\n ~-0.504\n\n","base.dists.exponential.Exponential":"\nbase.dists.exponential.Exponential( [λ] )\n Returns an exponential distribution object.\n\n Parameters\n ----------\n λ: number (optional)\n Rate parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n exponential: Object\n Distribution instance.\n\n exponential.lambda: number\n Rate parameter. If set, the value must be greater than `0`.\n\n exponential.entropy: number\n Read-only property which returns the differential entropy.\n\n exponential.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n exponential.mean: number\n Read-only property which returns the expected value.\n\n exponential.median: number\n Read-only property which returns the median.\n\n exponential.mode: number\n Read-only property which returns the mode.\n\n exponential.skewness: number\n Read-only property which returns the skewness.\n\n exponential.stdev: number\n Read-only property which returns the standard deviation.\n\n exponential.variance: number\n Read-only property which returns the variance.\n\n exponential.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n exponential.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n exponential.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n exponential.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n exponential.pdf: Function\n Evaluates the probability density function (PDF).\n\n exponential.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var exponential = base.dists.exponential.Exponential( 6.0 );\n > exponential.lambda\n 6.0\n > exponential.entropy\n ~-0.792\n > exponential.kurtosis\n 6.0\n > exponential.mean\n ~0.167\n > exponential.median\n ~0.116\n > exponential.mode\n 0.0\n > exponential.skewness\n 2.0\n > exponential.stdev\n ~0.167\n > exponential.variance\n ~0.028\n > exponential.cdf( 1.0 )\n ~0.998\n > exponential.logcdf( 1.0 )\n ~-0.002\n > exponential.logpdf( 1.5 )\n ~-7.208\n > exponential.mgf( -0.5 )\n ~0.923\n > exponential.pdf( 1.5 )\n ~0.001\n > exponential.quantile( 0.5 )\n ~0.116\n\n","base.dists.exponential.kurtosis":"\nbase.dists.exponential.kurtosis( λ )\n Returns the excess kurtosis of an exponential distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.exponential.kurtosis( 11.0 )\n 6.0\n > v = base.dists.exponential.kurtosis( 4.5 )\n 6.0\n\n","base.dists.exponential.logcdf":"\nbase.dists.exponential.logcdf( x, λ )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for an exponential distribution with rate parameter `λ` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.exponential.logcdf( 2.0, 0.1 )\n ~-1.708\n > y = base.dists.exponential.logcdf( 1.0, 2.0 )\n ~-0.145\n > y = base.dists.exponential.logcdf( -1.0, 4.0 )\n -Infinity\n > y = base.dists.exponential.logcdf( NaN, 1.0 )\n NaN\n > y = base.dists.exponential.logcdf( 0.0, NaN )\n NaN\n\n // Negative rate parameter:\n > y = base.dists.exponential.logcdf( 2.0, -1.0 )\n NaN\n\nbase.dists.exponential.logcdf.factory( λ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) for an exponential distribution with rate\n parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogCDF = base.dists.exponential.logcdf.factory( 0.5 );\n > var y = mylogCDF( 3.0 )\n ~-0.252\n\n","base.dists.exponential.logcdf.factory":"\nbase.dists.exponential.logcdf.factory( λ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) for an exponential distribution with rate\n parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogCDF = base.dists.exponential.logcdf.factory( 0.5 );\n > var y = mylogCDF( 3.0 )\n ~-0.252","base.dists.exponential.logpdf":"\nbase.dists.exponential.logpdf( x, λ )\n Evaluates the natural logarithm of the probability density function (PDF)\n for an exponential distribution with rate parameter `λ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.exponential.logpdf( 0.3, 4.0 )\n ~0.186\n > y = base.dists.exponential.logpdf( 2.0, 0.7 )\n ~-1.757\n > y = base.dists.exponential.logpdf( -1.0, 0.5 )\n -Infinity\n > y = base.dists.exponential.logpdf( 0, NaN )\n NaN\n > y = base.dists.exponential.logpdf( NaN, 2.0 )\n NaN\n\n // Negative rate:\n > y = base.dists.exponential.logpdf( 2.0, -1.0 )\n NaN\n\nbase.dists.exponential.logpdf.factory( λ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) for an exponential distribution with rate parameter\n `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.exponential.logpdf.factory( 0.5 );\n > var y = mylogpdf( 3.0 )\n ~-2.193\n\n","base.dists.exponential.logpdf.factory":"\nbase.dists.exponential.logpdf.factory( λ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) for an exponential distribution with rate parameter\n `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.exponential.logpdf.factory( 0.5 );\n > var y = mylogpdf( 3.0 )\n ~-2.193","base.dists.exponential.mean":"\nbase.dists.exponential.mean( λ )\n Returns the expected value of an exponential distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.exponential.mean( 11.0 )\n ~0.091\n > v = base.dists.exponential.mean( 4.5 )\n ~0.222\n\n","base.dists.exponential.median":"\nbase.dists.exponential.median( λ )\n Returns the median of an exponential distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.exponential.median( 11.0 )\n ~0.063\n > v = base.dists.exponential.median( 4.5 )\n ~0.154\n\n","base.dists.exponential.mgf":"\nbase.dists.exponential.mgf( t, λ )\n Evaluates the moment-generating function (MGF) for an exponential\n distribution with rate parameter `λ` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var v = base.dists.exponential.mgf( 2.0, 3.0 )\n 3.0\n > v = base.dists.exponential.mgf( 0.4, 1.2 )\n 1.5\n > v = base.dists.exponential.mgf( 0.8, 1.6 )\n 2.0\n > v = base.dists.exponential.mgf( 4.0, 3.0 )\n NaN\n > v = base.dists.exponential.mgf( NaN, 3.0 )\n NaN\n > v = base.dists.exponential.mgf( 2.0, NaN )\n NaN\n\n\nbase.dists.exponential.mgf.factory( λ )\n Returns a function for evaluating the moment-generating function (MGF) for\n an exponential distribution with rate parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n mg: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.exponential.mgf.factory( 4.0 );\n > var y = myMGF( 3.0 )\n 4.0\n > y = myMGF( 0.5 )\n ~1.143\n\n","base.dists.exponential.mgf.factory":"\nbase.dists.exponential.mgf.factory( λ )\n Returns a function for evaluating the moment-generating function (MGF) for\n an exponential distribution with rate parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n mg: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.exponential.mgf.factory( 4.0 );\n > var y = myMGF( 3.0 )\n 4.0\n > y = myMGF( 0.5 )\n ~1.143","base.dists.exponential.mode":"\nbase.dists.exponential.mode( λ )\n Returns the mode of an exponential distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.exponential.mode( 11.0 )\n 0.0\n > v = base.dists.exponential.mode( 4.5 )\n 0.0\n\n","base.dists.exponential.pdf":"\nbase.dists.exponential.pdf( x, λ )\n Evaluates the probability density function (PDF) for an exponential\n distribution with rate parameter `λ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.exponential.pdf( 0.3, 4.0 )\n ~1.205\n > y = base.dists.exponential.pdf( 2.0, 0.7 )\n ~0.173\n > y = base.dists.exponential.pdf( -1.0, 0.5 )\n 0.0\n > y = base.dists.exponential.pdf( 0, NaN )\n NaN\n > y = base.dists.exponential.pdf( NaN, 2.0 )\n NaN\n\n // Negative rate:\n > y = base.dists.exponential.pdf( 2.0, -1.0 )\n NaN\n\nbase.dists.exponential.pdf.factory( λ )\n Returns a function for evaluating the probability density function (PDF)\n for an exponential distribution with rate parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.exponential.pdf.factory( 0.5 );\n > var y = myPDF( 3.0 )\n ~0.112\n\n","base.dists.exponential.pdf.factory":"\nbase.dists.exponential.pdf.factory( λ )\n Returns a function for evaluating the probability density function (PDF)\n for an exponential distribution with rate parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.exponential.pdf.factory( 0.5 );\n > var y = myPDF( 3.0 )\n ~0.112","base.dists.exponential.quantile":"\nbase.dists.exponential.quantile( p, λ )\n Evaluates the quantile function for an exponential distribution with rate\n parameter `λ` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.exponential.quantile( 0.8, 1.0 )\n ~1.609\n > y = base.dists.exponential.quantile( 0.5, 4.0 )\n ~0.173\n > y = base.dists.exponential.quantile( 0.5, 0.1 )\n ~6.931\n\n > y = base.dists.exponential.quantile( -0.2, 0.1 )\n NaN\n\n > y = base.dists.exponential.quantile( NaN, 1.0 )\n NaN\n > y = base.dists.exponential.quantile( 0.0, NaN )\n NaN\n\n // Negative rate parameter:\n > y = base.dists.exponential.quantile( 0.5, -1.0 )\n NaN\n\n\nbase.dists.exponential.quantile.factory( λ )\n Returns a function for evaluating the quantile function for an exponential\n distribution with rate parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.exponential.quantile.factory( 0.4 );\n > var y = myQuantile( 0.4 )\n ~1.277\n > y = myQuantile( 1.0 )\n Infinity\n\n","base.dists.exponential.quantile.factory":"\nbase.dists.exponential.quantile.factory( λ )\n Returns a function for evaluating the quantile function for an exponential\n distribution with rate parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.exponential.quantile.factory( 0.4 );\n > var y = myQuantile( 0.4 )\n ~1.277\n > y = myQuantile( 1.0 )\n Infinity","base.dists.exponential.skewness":"\nbase.dists.exponential.skewness( λ )\n Returns the skewness of an exponential distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.exponential.skewness( 11.0 )\n 2.0\n > v = base.dists.exponential.skewness( 4.5 )\n 2.0\n\n","base.dists.exponential.stdev":"\nbase.dists.exponential.stdev( λ )\n Returns the standard deviation of an exponential distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.exponential.stdev( 9.0 )\n ~0.11\n > v = base.dists.exponential.stdev( 1.0 )\n 1.0\n\n","base.dists.exponential.variance":"\nbase.dists.exponential.variance( λ )\n Returns the variance of an exponential distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.exponential.variance( 9.0 )\n ~0.012\n > v = base.dists.exponential.variance( 1.0 )\n 1.0\n\n","base.dists.f.cdf":"\nbase.dists.f.cdf( x, d1, d2 )\n Evaluates the cumulative distribution function (CDF) for an F distribution\n with numerator degrees of freedom `d1` and denominator degrees of freedom\n `d2` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `d1 <= 0` or `d2 <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.f.cdf( 2.0, 1.0, 1.0 )\n ~0.608\n > var y = base.dists.f.cdf( 2.0, 8.0, 4.0 )\n ~0.737\n > var y = base.dists.f.cdf( -1.0, 2.0, 2.0 )\n 0.0\n > var y = base.dists.f.cdf( PINF, 4.0, 2.0 )\n 1.0\n > var y = base.dists.f.cdf( NINF, 4.0, 2.0 )\n 0.0\n\n > var y = base.dists.f.cdf( NaN, 1.0, 1.0 )\n NaN\n > var y = base.dists.f.cdf( 0.0, NaN, 1.0 )\n NaN\n > var y = base.dists.f.cdf( 0.0, 1.0, NaN )\n NaN\n\n > var y = base.dists.f.cdf( 2.0, 1.0, -1.0 )\n NaN\n > var y = base.dists.f.cdf( 2.0, -1.0, 1.0 )\n NaN\n\n\nbase.dists.f.cdf.factory( d1, d2 )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of an F distribution with numerator degrees of freedom `d1` and denominator\n degrees of freedom `d2`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.f.cdf.factory( 10.0, 2.0 );\n > var y = myCDF( 10.0 )\n ~0.906\n > y = myCDF( 8.0 )\n ~0.884\n\n","base.dists.f.cdf.factory":"\nbase.dists.f.cdf.factory( d1, d2 )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of an F distribution with numerator degrees of freedom `d1` and denominator\n degrees of freedom `d2`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.f.cdf.factory( 10.0, 2.0 );\n > var y = myCDF( 10.0 )\n ~0.906\n > y = myCDF( 8.0 )\n ~0.884","base.dists.f.entropy":"\nbase.dists.f.entropy( d1, d2 )\n Returns the differential entropy of an F distribution (in nats).\n\n If `d1 <= 0` or `d2 <= 0`, the function returns `NaN`.\n\n If `d1` or `d2` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.f.entropy( 3.0, 7.0 )\n ~1.298\n > v = base.dists.f.entropy( 4.0, 12.0 )\n ~1.12\n > v = base.dists.f.entropy( 8.0, 2.0 )\n ~2.144\n\n","base.dists.f.F":"\nbase.dists.f.F( [d1, d2] )\n Returns an F distribution object.\n\n Parameters\n ----------\n d1: number (optional)\n Numerator degrees of freedom. Must be greater than `0`. Default: `1.0`.\n\n d2: number (optional)\n Denominator degrees of freedom. Must be greater than `0`.\n Default: `1.0`.\n\n Returns\n -------\n f: Object\n Distribution instance.\n\n f.d1: number\n Numerator degrees of freedom. If set, the value must be greater than\n `0`.\n\n f.d2: number\n Denominator degrees of freedom. If set, the value must be greater than\n `0`.\n\n f.entropy: number\n Read-only property which returns the differential entropy.\n\n f.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n f.mean: number\n Read-only property which returns the expected value.\n\n f.mode: number\n Read-only property which returns the mode.\n\n f.skewness: number\n Read-only property which returns the skewness.\n\n f.stdev: number\n Read-only property which returns the standard deviation.\n\n f.variance: number\n Read-only property which returns the variance.\n\n f.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n f.pdf: Function\n Evaluates the probability density function (PDF).\n\n f.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var f = base.dists.f.F( 6.0, 9.0 );\n > f.d1\n 6.0\n > f.d2\n 9.0\n > f.entropy\n ~1.134\n > f.kurtosis\n ~104.564\n > f.mean\n ~1.286\n > f.mode\n ~0.545\n > f.skewness\n ~4.535\n > f.stdev\n ~1.197\n > f.variance\n ~1.433\n > f.cdf( 3.0 )\n ~0.932\n > f.pdf( 2.5 )\n ~0.095\n > f.quantile( 0.8 )\n ~1.826\n\n","base.dists.f.kurtosis":"\nbase.dists.f.kurtosis( d1, d2 )\n Returns the excess kurtosis of an F distribution.\n\n If `d1 <= 0` or `d2 <= 8`, the function returns `NaN`.\n\n If `d1` or `d2` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.f.kurtosis( 3.0, 9.0 )\n ~124.667\n > v = base.dists.f.kurtosis( 4.0, 12.0 )\n ~26.143\n > v = base.dists.f.kurtosis( 8.0, 9.0 )\n ~100.167\n\n","base.dists.f.mean":"\nbase.dists.f.mean( d1, d2 )\n Returns the expected value of an F distribution.\n\n If `d1 <= 0` or `d2 <= 2`, the function returns `NaN`.\n\n If `d1` or `d2` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.f.mean( 3.0, 5.0 )\n ~1.667\n > v = base.dists.f.mean( 4.0, 12.0 )\n ~1.2\n > v = base.dists.f.mean( 8.0, 4.0 )\n 2.0\n\n","base.dists.f.mode":"\nbase.dists.f.mode( d1, d2 )\n Returns the mode of an F distribution.\n\n If `d1 <= 2` or `d2 <= 0`, the function returns `NaN`.\n\n If `d1` or `d2` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.f.mode( 3.0, 5.0 )\n ~0.238\n > v = base.dists.f.mode( 4.0, 12.0 )\n ~0.429\n > v = base.dists.f.mode( 8.0, 4.0 )\n 0.5\n\n","base.dists.f.pdf":"\nbase.dists.f.pdf( x, d1, d2 )\n Evaluates the probability density function (PDF) for an F distribution with\n numerator degrees of freedom `d1` and denominator degrees of freedom `d2` at\n a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `d1 <= 0` or `d2 <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.f.pdf( 2.0, 0.5, 1.0 )\n ~0.057\n > y = base.dists.f.pdf( 0.1, 1.0, 1.0 )\n ~0.915\n > y = base.dists.f.pdf( -1.0, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.f.pdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.f.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.f.pdf( 0.0, 1.0, NaN )\n NaN\n\n > y = base.dists.f.pdf( 2.0, 1.0, -1.0 )\n NaN\n > y = base.dists.f.pdf( 2.0, -1.0, 1.0 )\n NaN\n\n\nbase.dists.f.pdf.factory( d1, d2 )\n Returns a function for evaluating the probability density function (PDF) of\n an F distribution with numerator degrees of freedom `d1` and denominator\n degrees of freedom `d2`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.f.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 7.0 )\n ~0.004\n > y = myPDF( 2.0 )\n ~0.166\n\n","base.dists.f.pdf.factory":"\nbase.dists.f.pdf.factory( d1, d2 )\n Returns a function for evaluating the probability density function (PDF) of\n an F distribution with numerator degrees of freedom `d1` and denominator\n degrees of freedom `d2`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.f.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 7.0 )\n ~0.004\n > y = myPDF( 2.0 )\n ~0.166","base.dists.f.quantile":"\nbase.dists.f.quantile( p, d1, d2 )\n Evaluates the quantile function for an F distribution with numerator degrees\n of freedom `d1` and denominator degrees of freedom `d2` at a probability\n `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `d1 <= 0` or `d2 <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.f.quantile( 0.8, 1.0, 1.0 )\n ~9.472\n > y = base.dists.f.quantile( 0.5, 4.0, 2.0 )\n ~1.207\n\n > y = base.dists.f.quantile( 1.1, 1.0, 1.0 )\n NaN\n > y = base.dists.f.quantile( -0.2, 1.0, 1.0 )\n NaN\n\n > y = base.dists.f.quantile( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.f.quantile( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.f.quantile( 0.5, 1.0, NaN )\n NaN\n\n > y = base.dists.f.quantile( 0.5, -1.0, 1.0 )\n NaN\n > y = base.dists.f.quantile( 0.5, 1.0, -1.0 )\n NaN\n\n\nbase.dists.f.quantile.factory( d1, d2 )\n Returns a function for evaluating the quantile function of an F distribution\n with numerator degrees of freedom `d1` and denominator degrees of freedom\n `d2`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.f.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.2 )\n ~0.527\n > y = myQuantile( 0.8 )\n ~4.382\n\n","base.dists.f.quantile.factory":"\nbase.dists.f.quantile.factory( d1, d2 )\n Returns a function for evaluating the quantile function of an F distribution\n with numerator degrees of freedom `d1` and denominator degrees of freedom\n `d2`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.f.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.2 )\n ~0.527\n > y = myQuantile( 0.8 )\n ~4.382","base.dists.f.skewness":"\nbase.dists.f.skewness( d1, d2 )\n Returns the skewness of an F distribution.\n\n If `d1 <= 0` or `d2 <= 6`, the function returns `NaN`.\n\n If `d1` or `d2` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.f.skewness( 3.0, 7.0 )\n 11.0\n > v = base.dists.f.skewness( 4.0, 12.0 )\n ~3.207\n > v = base.dists.f.skewness( 8.0, 7.0 )\n ~10.088\n\n","base.dists.f.stdev":"\nbase.dists.f.stdev( d1, d2 )\n Returns the standard deviation of an F distribution.\n\n If `d1 <= 0` or `d2 <= 4`, the function returns `NaN`.\n\n If `d1` or `d2` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.f.stdev( 3.0, 5.0 )\n ~3.333\n > v = base.dists.f.stdev( 4.0, 12.0 )\n ~1.122\n > v = base.dists.f.stdev( 8.0, 5.0 )\n ~2.764\n\n","base.dists.f.variance":"\nbase.dists.f.variance( d1, d2 )\n Returns the variance of an F distribution.\n\n If `d1 <= 0` or `d2 <= 4`, the function returns `NaN`.\n\n If `d1` or `d2` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n d1: number\n Numerator degrees of freedom.\n\n d2: number\n Denominator degrees of freedom.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.f.variance( 3.0, 5.0 )\n ~11.111\n > v = base.dists.f.variance( 4.0, 12.0 )\n ~1.26\n > v = base.dists.f.variance( 8.0, 5.0 )\n ~7.639\n\n","base.dists.frechet.cdf":"\nbase.dists.frechet.cdf( x, α, s, m )\n Evaluates the cumulative distribution function (CDF) for a Fréchet\n distribution with shape parameter `α`, scale parameter `s`, and location\n `m`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.frechet.cdf( 10.0, 2.0, 3.0, 0.0 )\n ~0.914\n > y = base.dists.frechet.cdf( -1.0, 2.0, 3.0, -3.0 )\n ~0.105\n > y = base.dists.frechet.cdf( 2.5, 2.0, 1.0, 2.0 )\n ~0.018\n > y = base.dists.frechet.cdf( NaN, 1.0, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.cdf( 0.0, NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.cdf( 0.0, 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.cdf( 0.0, 1.0, 1.0, NaN )\n NaN\n > y = base.dists.frechet.cdf( 0.0, -1.0, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.cdf( 0.0, 1.0, -1.0, 0.0 )\n NaN\n\n\nbase.dists.frechet.cdf.factory( α, s, m )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Fréchet distribution with shape parameter `α`, scale parameter `s`, and\n location `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.frechet.cdf.factory( 3.0, 3.0, 5.0 );\n > var y = myCDF( 10.0 )\n ~0.806\n > y = myCDF( 7.0 )\n ~0.034\n\n","base.dists.frechet.cdf.factory":"\nbase.dists.frechet.cdf.factory( α, s, m )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Fréchet distribution with shape parameter `α`, scale parameter `s`, and\n location `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.frechet.cdf.factory( 3.0, 3.0, 5.0 );\n > var y = myCDF( 10.0 )\n ~0.806\n > y = myCDF( 7.0 )\n ~0.034","base.dists.frechet.entropy":"\nbase.dists.frechet.entropy( α, s, m )\n Returns the differential entropy of a Fréchet distribution with shape\n parameter `α`, scale parameter `s`, and location `m` (in nats).\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var y = base.dists.frechet.entropy( 1.0, 1.0, 1.0 )\n ~2.154\n > y = base.dists.frechet.entropy( 4.0, 2.0, 1.0 )\n ~1.028\n > y = base.dists.frechet.entropy( NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.entropy( 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.entropy( 1.0, 1.0, NaN )\n NaN\n\n","base.dists.frechet.Frechet":"\nbase.dists.frechet.Frechet( [α, s, m] )\n Returns a Fréchet distribution object.\n\n Parameters\n ----------\n α: number (optional)\n Shape parameter. Must be greater than `0`. Default: `1.0`.\n\n s: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n m: number (optional)\n Location parameter. Default: `0.0`.\n\n Returns\n -------\n frechet: Object\n Distribution instance.\n\n frechet.alpha: number\n Shape parameter. If set, the value must be greater than `0`.\n\n frechet.s: number\n Scale parameter. If set, the value must be greater than `0`.\n\n frechet.m: number\n Location parameter.\n\n frechet.entropy: number\n Read-only property which returns the differential entropy.\n\n frechet.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n frechet.mean: number\n Read-only property which returns the expected value.\n\n frechet.median: number\n Read-only property which returns the median.\n\n frechet.mode: number\n Read-only property which returns the mode.\n\n frechet.skewness: number\n Read-only property which returns the skewness.\n\n frechet.stdev: number\n Read-only property which returns the standard deviation.\n\n frechet.variance: number\n Read-only property which returns the variance.\n\n frechet.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n frechet.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n frechet.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n frechet.pdf: Function\n Evaluates the probability density function (PDF).\n\n frechet.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var frechet = base.dists.frechet.Frechet( 1.0, 1.0, 0.0 );\n > frechet.alpha\n 1.0\n > frechet.s\n 1.0\n > frechet.m\n 0.0\n > frechet.entropy\n ~2.154\n > frechet.kurtosis\n Infinity\n > frechet.mean\n Infinity\n > frechet.median\n ~1.443\n > frechet.mode\n 0.5\n > frechet.skewness\n Infinity\n > frechet.stdev\n Infinity\n > frechet.variance\n Infinity\n > frechet.cdf( 0.8 )\n ~0.287\n > frechet.logcdf( 0.8 )\n -1.25\n > frechet.logpdf( 0.8 )\n ~-0.804\n > frechet.pdf( 0.8 )\n ~0.448\n > frechet.quantile( 0.8 )\n ~4.481\n\n","base.dists.frechet.kurtosis":"\nbase.dists.frechet.kurtosis( α, s, m )\n Returns the excess kurtosis of a Fréchet distribution with shape parameter\n `α`, scale parameter `s`, and location `m`.\n\n If provided `0 < α <= 4` and `s > 0`, the function returns positive\n infinity.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var y = base.dists.frechet.kurtosis( 5.0, 2.0, 1.0 )\n ~45.092\n > var y = base.dists.frechet.kurtosis( 5.0, 10.0, -3.0 )\n ~45.092\n > y = base.dists.frechet.kurtosis( 3.5, 2.0, 1.0 )\n Infinity\n > y = base.dists.frechet.kurtosis( NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.kurtosis( 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.kurtosis( 1.0, 1.0, NaN )\n NaN\n\n","base.dists.frechet.logcdf":"\nbase.dists.frechet.logcdf( x, α, s, m )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a Fréchet distribution with shape parameter `α`, scale parameter\n `s`, and location `m`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.frechet.logcdf( 10.0, 2.0, 3.0, 0.0 )\n ~-0.09\n > y = base.dists.frechet.logcdf( -1.0, 2.0, 3.0, -3.0 )\n ~-2.25\n > y = base.dists.frechet.logcdf( 2.5, 2.0, 1.0, 2.0 )\n -4.0\n > y = base.dists.frechet.logcdf( NaN, 1.0, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.logcdf( 0.0, NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.logcdf( 0.0, 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.logcdf( 0.0, 1.0, 1.0, NaN )\n NaN\n > y = base.dists.frechet.logcdf( 0.0, -1.0, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.logcdf( 0.0, 1.0, -1.0, 0.0 )\n NaN\n\n\nbase.dists.frechet.logcdf.factory( α, s, m )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a Fréchet distribution with shape parameter\n `α`, scale parameter `s`, and location `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.frechet.logcdf.factory( 3.0, 3.0, 5.0 );\n > var y = mylogcdf( 10.0 )\n ~-0.216\n > y = mylogcdf( 7.0 )\n ~-3.375\n\n","base.dists.frechet.logcdf.factory":"\nbase.dists.frechet.logcdf.factory( α, s, m )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a Fréchet distribution with shape parameter\n `α`, scale parameter `s`, and location `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.frechet.logcdf.factory( 3.0, 3.0, 5.0 );\n > var y = mylogcdf( 10.0 )\n ~-0.216\n > y = mylogcdf( 7.0 )\n ~-3.375","base.dists.frechet.logpdf":"\nbase.dists.frechet.logpdf( x, α, s, m )\n Evaluates the logarithm of the probability density function (PDF) for a\n Fréchet distribution with shape parameter `α`, scale parameter `s`, and\n location `m`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.frechet.logpdf( 10.0, 1.0, 3.0, 5.0 )\n ~-2.72\n > y = base.dists.frechet.logpdf( -2.0, 1.0, 3.0, -3.0 )\n ~-1.901\n > y = base.dists.frechet.logpdf( 0.0, 2.0, 1.0, -1.0 )\n ~-0.307\n > y = base.dists.frechet.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.frechet.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.frechet.logpdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.frechet.logpdf( 0.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.frechet.logpdf.factory( α, s, m )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Fréchet distribution with shape parameter `α`, scale\n parameter `s`, and location `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.frechet.logpdf.factory( 2.0, 3.0, 1.0 );\n > var y = mylogPDF( 10.0 )\n ~-3.812\n > y = mylogPDF( 2.0 )\n ~-6.11\n\n","base.dists.frechet.logpdf.factory":"\nbase.dists.frechet.logpdf.factory( α, s, m )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Fréchet distribution with shape parameter `α`, scale\n parameter `s`, and location `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.frechet.logpdf.factory( 2.0, 3.0, 1.0 );\n > var y = mylogPDF( 10.0 )\n ~-3.812\n > y = mylogPDF( 2.0 )\n ~-6.11","base.dists.frechet.mean":"\nbase.dists.frechet.mean( α, s, m )\n Returns the expected value of a Fréchet distribution with shape parameter\n `α`, scale parameter `s`, and location `m`.\n\n If provided `0 < α <= 1` and `s > 0`, the function returns positive\n infinity.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Mean.\n\n Examples\n --------\n > var y = base.dists.frechet.mean( 4.0, 2.0, 1.0 )\n ~3.451\n > y = base.dists.frechet.mean( 0.5, 2.0, 1.0 )\n Infinity\n > y = base.dists.frechet.mean( NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.mean( 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.mean( 1.0, 1.0, NaN )\n NaN\n\n","base.dists.frechet.median":"\nbase.dists.frechet.median( α, s, m )\n Returns the median of a Fréchet distribution with shape parameter\n `α`, scale parameter `s`, and location `m`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var y = base.dists.frechet.median( 4.0, 2.0, 1.0 )\n ~3.192\n > var y = base.dists.frechet.median( 4.0, 2.0, -3.0 )\n ~-0.808\n > y = base.dists.frechet.median( 0.5, 2.0, 1.0 )\n ~5.163\n > y = base.dists.frechet.median( NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.median( 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.median( 1.0, 1.0, NaN )\n NaN\n\n","base.dists.frechet.mode":"\nbase.dists.frechet.mode( α, s, m )\n Returns the mode of a Fréchet distribution with shape parameter `α`, scale\n parameter `s`, and location `m`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var y = base.dists.frechet.mode( 4.0, 2.0, 1.0 )\n ~2.891\n > var y = base.dists.frechet.mode( 4.0, 2.0, -3.0 )\n ~-1.109\n > y = base.dists.frechet.mode( 0.5, 2.0, 1.0 )\n ~1.222\n > y = base.dists.frechet.mode( NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.mode( 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.mode( 1.0, 1.0, NaN )\n NaN\n\n","base.dists.frechet.pdf":"\nbase.dists.frechet.pdf( x, α, s, m )\n Evaluates the probability density function (PDF) for a Fréchet distribution\n with shape parameter `α`, scale parameter `s`, and location `m`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.frechet.pdf( 10.0, 0.0, 3.0 )\n ~0.965\n > y = base.dists.frechet.pdf( -2.0, 0.0, 3.0 )\n ~0.143\n > y = base.dists.frechet.pdf( 0.0, 0.0, 1.0 )\n ~0.368\n > y = base.dists.frechet.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.frechet.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.frechet.pdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.frechet.pdf( 0.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.frechet.pdf.factory( α, s, m )\n Returns a function for evaluating the probability density function (PDF) of\n a Fréchet distribution with shape parameter `α`, scale parameter `s`, and\n location `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.frechet.pdf.factory( 2.0, 3.0 );\n > var y = myPDF( 10.0 )\n ~0.933\n > y = myPDF( 2.0 )\n ~0.368\n\n","base.dists.frechet.pdf.factory":"\nbase.dists.frechet.pdf.factory( α, s, m )\n Returns a function for evaluating the probability density function (PDF) of\n a Fréchet distribution with shape parameter `α`, scale parameter `s`, and\n location `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.frechet.pdf.factory( 2.0, 3.0 );\n > var y = myPDF( 10.0 )\n ~0.933\n > y = myPDF( 2.0 )\n ~0.368","base.dists.frechet.quantile":"\nbase.dists.frechet.quantile( p, α, s, m )\n Evaluates the quantile function for a Fréchet distribution with shape\n parameter `α`, scale parameter `s`, and location `m`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.frechet.quantile( 0.3, 10.0, 2.0, 3.0 )\n ~4.963\n > y = base.dists.frechet.quantile( 0.2, 3.0, 3.0, 3.0 )\n ~5.56\n > y = base.dists.frechet.quantile( 0.9, 1.0, 1.0, -3.0 )\n ~6.491\n > y = base.dists.frechet.quantile( NaN, 1.0, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.quantile( 0.0, NaN, 1.0, 0.0)\n NaN\n > y = base.dists.frechet.quantile( 0.0, 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.quantile( 0.0, 1.0, 1.0, NaN )\n NaN\n > y = base.dists.frechet.quantile( 0.0, -1.0, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.quantile( 0.0, 1.0, -1.0, 0.0 )\n NaN\n\n\nbase.dists.frechet.quantile.factory( α, s, m )\n Returns a function for evaluating the quantile function of a Fréchet\n distribution with shape parameter `α`, scale parameter `s`, and location\n `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.frechet.quantile.factory( 2.0, 2.0, 3.0 );\n > var y = myQuantile( 0.5 )\n ~5.402\n > y = myQuantile( 0.2 )\n ~4.576\n\n","base.dists.frechet.quantile.factory":"\nbase.dists.frechet.quantile.factory( α, s, m )\n Returns a function for evaluating the quantile function of a Fréchet\n distribution with shape parameter `α`, scale parameter `s`, and location\n `m`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.frechet.quantile.factory( 2.0, 2.0, 3.0 );\n > var y = myQuantile( 0.5 )\n ~5.402\n > y = myQuantile( 0.2 )\n ~4.576","base.dists.frechet.skewness":"\nbase.dists.frechet.skewness( α, s, m )\n Returns the skewness of a Fréchet distribution with shape parameter `α`,\n scale parameter `s`, and location `m`.\n\n If provided `0 < α <= 3` and `s > 0`, the function returns positive\n infinity.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var y = base.dists.frechet.skewness( 4.0, 2.0, 1.0 )\n ~5.605\n > var y = base.dists.frechet.skewness( 4.0, 2.0, -3.0 )\n ~5.605\n > y = base.dists.frechet.skewness( 0.5, 2.0, 1.0 )\n Infinity\n > y = base.dists.frechet.skewness( NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.skewness( 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.skewness( 1.0, 1.0, NaN )\n NaN\n\n","base.dists.frechet.stdev":"\nbase.dists.frechet.stdev( α, s, m )\n Returns the standard deviation of a Fréchet distribution with shape\n parameter `α`, scale parameter `s`, and location `m`.\n\n If provided `0 < α <= 2` and `s > 0`, the function returns positive\n infinity.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var y = base.dists.frechet.stdev( 4.0, 2.0, 1.0 )\n ~1.041\n > var y = base.dists.frechet.stdev( 4.0, 2.0, -3.0 )\n ~1.041\n > y = base.dists.frechet.stdev( 0.5, 2.0, 1.0 )\n Infinity\n > y = base.dists.frechet.stdev( NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.stdev( 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.stdev( 1.0, 1.0, NaN )\n NaN\n\n","base.dists.frechet.variance":"\nbase.dists.frechet.variance( α, s, m )\n Returns the variance of a Fréchet distribution with shape parameter `α`,\n scale parameter `s`, and location `m`.\n\n If provided `0 < α <= 2` and `s > 0`, the function returns positive\n infinity.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `α <= 0` or `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n s: number\n Scale parameter.\n\n m: number\n Location parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var y = base.dists.frechet.variance( 4.0, 2.0, 1.0 )\n ~1.083\n > var y = base.dists.frechet.variance( 4.0, 2.0, -3.0 )\n ~1.083\n > y = base.dists.frechet.variance( 0.5, 2.0, 1.0 )\n Infinity\n > y = base.dists.frechet.variance( NaN, 1.0, 0.0 )\n NaN\n > y = base.dists.frechet.variance( 1.0, NaN, 0.0 )\n NaN\n > y = base.dists.frechet.variance( 1.0, 1.0, NaN )\n NaN\n\n","base.dists.gamma.cdf":"\nbase.dists.gamma.cdf( x, α, β )\n Evaluates the cumulative distribution function (CDF) for a gamma\n distribution with shape parameter `α` and rate parameter `β` at a value `x`.\n\n If `α < 0` or `β <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.gamma.cdf( 2.0, 1.0, 1.0 )\n ~0.865\n > y = base.dists.gamma.cdf( 2.0, 3.0, 1.0 )\n ~0.323\n > y = base.dists.gamma.cdf( -1.0, 2.0, 2.0 )\n 0.0\n > y = base.dists.gamma.cdf( PINF, 4.0, 2.0 )\n 1.0\n > y = base.dists.gamma.cdf( NINF, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.gamma.cdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.gamma.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gamma.cdf( 0.0, 0.0, NaN )\n NaN\n\n > y = base.dists.gamma.cdf( 2.0, -1.0, 1.0 )\n NaN\n > y = base.dists.gamma.cdf( 2.0, 1.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `0` when `α = 0.0`:\n > y = base.dists.gamma.cdf( 2.0, 0.0, 2.0 )\n 1.0\n > y = base.dists.gamma.cdf( -2.0, 0.0, 2.0 )\n 0.0\n > y = base.dists.gamma.cdf( 0.0, 0.0, 2.0 )\n 0.0\n\n\nbase.dists.gamma.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a gamma distribution with shape parameter `α` and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.gamma.cdf.factory( 2.0, 0.5 );\n > var y = myCDF( 6.0 )\n ~0.801\n > y = myCDF( 2.0 )\n ~0.264\n\n","base.dists.gamma.cdf.factory":"\nbase.dists.gamma.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a gamma distribution with shape parameter `α` and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.gamma.cdf.factory( 2.0, 0.5 );\n > var y = myCDF( 6.0 )\n ~0.801\n > y = myCDF( 2.0 )\n ~0.264","base.dists.gamma.entropy":"\nbase.dists.gamma.entropy( α, β )\n Returns the differential entropy of a gamma distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.gamma.entropy( 1.0, 1.0 )\n 1.0\n > v = base.dists.gamma.entropy( 4.0, 12.0 )\n ~-0.462\n > v = base.dists.gamma.entropy( 8.0, 2.0 )\n ~1.723\n\n","base.dists.gamma.Gamma":"\nbase.dists.gamma.Gamma( [α, β] )\n Returns a gamma distribution object.\n\n Parameters\n ----------\n α: number (optional)\n Shape parameter. Must be greater than `0`. Default: `1.0`.\n\n β: number (optional)\n Rate parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n gamma: Object\n Distribution instance.\n\n gamma.alpha: number\n Shape parameter. If set, the value must be greater than `0`.\n\n gamma.beta: number\n Rate parameter. If set, the value must be greater than `0`.\n\n gamma.entropy: number\n Read-only property which returns the differential entropy.\n\n gamma.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n gamma.mean: number\n Read-only property which returns the expected value.\n\n gamma.mode: number\n Read-only property which returns the mode.\n\n gamma.skewness: number\n Read-only property which returns the skewness.\n\n gamma.stdev: number\n Read-only property which returns the standard deviation.\n\n gamma.variance: number\n Read-only property which returns the variance.\n\n gamma.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n gamma.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n gamma.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n gamma.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n gamma.pdf: Function\n Evaluates the probability density function (PDF).\n\n gamma.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var gamma = base.dists.gamma.Gamma( 6.0, 5.0 );\n > gamma.alpha\n 6.0\n > gamma.beta\n 5.0\n > gamma.entropy\n ~0.647\n > gamma.kurtosis\n 1.0\n > gamma.mean\n 1.2\n > gamma.mode\n 1.0\n > gamma.skewness\n ~0.816\n > gamma.stdev\n ~0.49\n > gamma.variance\n 0.24\n > gamma.cdf( 0.8 )\n ~0.215\n > gamma.logcdf( 0.8 )\n ~-1.538\n > gamma.logpdf( 1.0 )\n ~-0.131\n > gamma.mgf( -0.5 )\n ~0.564\n > gamma.pdf( 1.0 )\n ~0.877\n > gamma.quantile( 0.8 )\n ~1.581\n\n","base.dists.gamma.kurtosis":"\nbase.dists.gamma.kurtosis( α, β )\n Returns the excess kurtosis of a gamma distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.gamma.kurtosis( 1.0, 1.0 )\n 6.0\n > v = base.dists.gamma.kurtosis( 4.0, 12.0 )\n 1.5\n > v = base.dists.gamma.kurtosis( 8.0, 2.0 )\n 0.75\n\n","base.dists.gamma.logcdf":"\nbase.dists.gamma.logcdf( x, α, β )\n Evaluates the logarithm of the cumulative distribution function (CDF) for a\n gamma distribution with shape parameter `α` and rate parameter `β` at a\n value `x`.\n\n If `α < 0` or `β <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.gamma.logcdf( 2.0, 0.5, 1.0 )\n ~-0.047\n > y = base.dists.gamma.logcdf( 0.1, 1.0, 1.0 )\n ~-2.352\n > y = base.dists.gamma.logcdf( -1.0, 4.0, 2.0 )\n -Infinity\n\n > y = base.dists.gamma.logcdf( NaN, 0.6, 1.0 )\n NaN\n > y = base.dists.gamma.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gamma.logcdf( 0.0, 1.0, NaN )\n NaN\n\n // Negative shape parameter:\n > y = base.dists.gamma.logcdf( 2.0, -1.0, 1.0 )\n NaN\n // Non-positive rate parameter:\n > y = base.dists.gamma.logcdf( 2.0, 1.0, -1.0 )\n NaN\n\n\nbase.dists.gamma.logcdf.factory( α, β )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a gamma distribution with shape parameter `α`\n and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogCDF = base.dists.gamma.logcdf.factory( 6.0, 7.0 );\n > var y = mylogCDF( 2.0 )\n ~-0.006\n\n","base.dists.gamma.logcdf.factory":"\nbase.dists.gamma.logcdf.factory( α, β )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a gamma distribution with shape parameter `α`\n and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogCDF = base.dists.gamma.logcdf.factory( 6.0, 7.0 );\n > var y = mylogCDF( 2.0 )\n ~-0.006","base.dists.gamma.logpdf":"\nbase.dists.gamma.logpdf( x, α, β )\n Evaluates the logarithm of the probability density function (PDF) for a\n gamma distribution with shape parameter `α` and rate parameter `β` at a\n value `x`.\n\n If `α < 0` or `β <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.gamma.logpdf( 2.0, 0.5, 1.0 )\n ~-2.919\n > y = base.dists.gamma.logpdf( 0.1, 1.0, 1.0 )\n ~-0.1\n > y = base.dists.gamma.logpdf( -1.0, 4.0, 2.0 )\n -Infinity\n\n > y = base.dists.gamma.logpdf( NaN, 0.6, 1.0 )\n NaN\n > y = base.dists.gamma.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gamma.logpdf( 0.0, 1.0, NaN )\n NaN\n\n // Negative shape parameter:\n > y = base.dists.gamma.logpdf( 2.0, -1.0, 1.0 )\n NaN\n // Non-positive rate parameter:\n > y = base.dists.gamma.logpdf( 2.0, 1.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `0.0` when `α = 0.0`:\n > y = base.dists.gamma.logpdf( 2.0, 0.0, 2.0 )\n -Infinity\n > y = base.dists.gamma.logpdf( 0.0, 0.0, 2.0 )\n Infinity\n\n\nbase.dists.gamma.logpdf.factory( α, β )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a gamma distribution with shape parameter `α` and rate\n parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.gamma.logpdf.factory( 6.0, 7.0 );\n > var y = mylogPDF( 2.0 )\n ~-3.646\n\n","base.dists.gamma.logpdf.factory":"\nbase.dists.gamma.logpdf.factory( α, β )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a gamma distribution with shape parameter `α` and rate\n parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.gamma.logpdf.factory( 6.0, 7.0 );\n > var y = mylogPDF( 2.0 )\n ~-3.646","base.dists.gamma.mean":"\nbase.dists.gamma.mean( α, β )\n Returns the expected value of a gamma distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.gamma.mean( 1.0, 1.0 )\n 1.0\n > v = base.dists.gamma.mean( 4.0, 12.0 )\n ~0.333\n > v = base.dists.gamma.mean( 8.0, 2.0 )\n 4.0\n\n","base.dists.gamma.mgf":"\nbase.dists.gamma.mgf( t, α, β )\n Evaluates the moment-generating function (MGF) for a gamma distribution with\n shape parameter `α` and rate parameter `β` at a value `t`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.gamma.mgf( 0.5, 0.5, 1.0 )\n ~1.414\n > y = base.dists.gamma.mgf( 0.1, 1.0, 1.0 )\n ~1.111\n > y = base.dists.gamma.mgf( -1.0, 4.0, 2.0 )\n ~0.198\n\n > y = base.dists.gamma.mgf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.gamma.mgf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gamma.mgf( 0.0, 1.0, NaN )\n NaN\n\n > y = base.dists.gamma.mgf( 2.0, 4.0, 1.0 )\n NaN\n > y = base.dists.gamma.mgf( 2.0, -0.5, 1.0 )\n NaN\n > y = base.dists.gamma.mgf( 2.0, 1.0, 0.0 )\n NaN\n > y = base.dists.gamma.mgf( 2.0, 1.0, -1.0 )\n NaN\n\n\nbase.dists.gamma.mgf.factory( α, β )\n Returns a function for evaluating the moment-generating function (MGF) of a\n gamma distribution with shape parameter `α` and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.gamma.mgf.factory( 3.0, 1.5 );\n > var y = myMGF( 1.0 )\n ~27.0\n > y = myMGF( 0.5 )\n ~3.375\n\n","base.dists.gamma.mgf.factory":"\nbase.dists.gamma.mgf.factory( α, β )\n Returns a function for evaluating the moment-generating function (MGF) of a\n gamma distribution with shape parameter `α` and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.gamma.mgf.factory( 3.0, 1.5 );\n > var y = myMGF( 1.0 )\n ~27.0\n > y = myMGF( 0.5 )\n ~3.375","base.dists.gamma.mode":"\nbase.dists.gamma.mode( α, β )\n Returns the mode of a gamma distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.gamma.mode( 1.0, 1.0 )\n 0.0\n > v = base.dists.gamma.mode( 4.0, 12.0 )\n 0.25\n > v = base.dists.gamma.mode( 8.0, 2.0 )\n 3.5\n\n","base.dists.gamma.pdf":"\nbase.dists.gamma.pdf( x, α, β )\n Evaluates the probability density function (PDF) for a gamma distribution\n with shape parameter `α` and rate parameter `β` at a value `x`.\n\n If `α < 0` or `β <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.gamma.pdf( 2.0, 0.5, 1.0 )\n ~0.054\n > y = base.dists.gamma.pdf( 0.1, 1.0, 1.0 )\n ~0.905\n > y = base.dists.gamma.pdf( -1.0, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.gamma.pdf( NaN, 0.6, 1.0 )\n NaN\n > y = base.dists.gamma.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gamma.pdf( 0.0, 1.0, NaN )\n NaN\n\n // Negative shape parameter:\n > y = base.dists.gamma.pdf( 2.0, -1.0, 1.0 )\n NaN\n // Non-positive rate parameter:\n > y = base.dists.gamma.pdf( 2.0, 1.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `0.0` when `α = 0.0`:\n > y = base.dists.gamma.pdf( 2.0, 0.0, 2.0 )\n 0.0\n > y = base.dists.gamma.pdf( 0.0, 0.0, 2.0 )\n Infinity\n\n\nbase.dists.gamma.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF) of\n a gamma distribution with shape parameter `α` and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.gamma.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 2.0 )\n ~0.026\n\n","base.dists.gamma.pdf.factory":"\nbase.dists.gamma.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF) of\n a gamma distribution with shape parameter `α` and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.gamma.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 2.0 )\n ~0.026","base.dists.gamma.quantile":"\nbase.dists.gamma.quantile( p, α, β )\n Evaluates the quantile function for a gamma distribution with shape\n parameter `α` and rate parameter `β` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If `α < 0` or `β <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.gamma.quantile( 0.8, 2.0, 1.0 )\n ~2.994\n > y = base.dists.gamma.quantile( 0.5, 4.0, 2.0 )\n ~1.836\n\n > y = base.dists.gamma.quantile( 1.1, 1.0, 1.0 )\n NaN\n > y = base.dists.gamma.quantile( -0.2, 1.0, 1.0 )\n NaN\n\n > y = base.dists.gamma.quantile( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.gamma.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gamma.quantile( 0.0, 1.0, NaN )\n NaN\n\n // Non-positive shape parameter:\n > y = base.dists.gamma.quantile( 0.5, -1.0, 1.0 )\n NaN\n // Non-positive rate parameter:\n > y = base.dists.gamma.quantile( 0.5, 1.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `0.0` when `α = 0.0`:\n > y = base.dists.gamma.quantile( 0.3, 0.0, 2.0 )\n 0.0\n > y = base.dists.gamma.quantile( 0.9, 0.0, 2.0 )\n 0.0\n\n\nbase.dists.gamma.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of a gamma\n distribution with shape parameter `α` and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.gamma.quantile.factory( 2.0, 2.0 );\n > var y = myQuantile( 0.8 )\n ~1.497\n > y = myQuantile( 0.4 )\n ~0.688\n\n","base.dists.gamma.quantile.factory":"\nbase.dists.gamma.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of a gamma\n distribution with shape parameter `α` and rate parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.gamma.quantile.factory( 2.0, 2.0 );\n > var y = myQuantile( 0.8 )\n ~1.497\n > y = myQuantile( 0.4 )\n ~0.688","base.dists.gamma.skewness":"\nbase.dists.gamma.skewness( α, β )\n Returns the skewness of a gamma distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.gamma.skewness( 1.0, 1.0 )\n 2.0\n > v = base.dists.gamma.skewness( 4.0, 12.0 )\n 1.0\n > v = base.dists.gamma.skewness( 8.0, 2.0 )\n ~0.707\n\n","base.dists.gamma.stdev":"\nbase.dists.gamma.stdev( α, β )\n Returns the standard deviation of a gamma distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.gamma.stdev( 1.0, 1.0 )\n 1.0\n > v = base.dists.gamma.stdev( 4.0, 12.0 )\n ~0.167\n > v = base.dists.gamma.stdev( 8.0, 2.0 )\n ~1.414\n\n","base.dists.gamma.variance":"\nbase.dists.gamma.variance( α, β )\n Returns the variance of a gamma distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.gamma.variance( 1.0, 1.0 )\n 1.0\n > v = base.dists.gamma.variance( 4.0, 12.0 )\n ~0.028\n > v = base.dists.gamma.variance( 8.0, 2.0 )\n 2.0\n\n","base.dists.geometric.cdf":"\nbase.dists.geometric.cdf( x, p )\n Evaluates the cumulative distribution function (CDF) for a geometric\n distribution with success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.geometric.cdf( 2.0, 0.5 )\n 0.875\n > y = base.dists.geometric.cdf( 2.0, 0.1 )\n ~0.271\n > y = base.dists.geometric.cdf( -1.0, 4.0 )\n 0.0\n > y = base.dists.geometric.cdf( NaN, 0.5 )\n NaN\n > y = base.dists.geometric.cdf( 0.0, NaN )\n NaN\n // Invalid probability\n > y = base.dists.geometric.cdf( 2.0, 1.4 )\n NaN\n\n\nbase.dists.geometric.cdf.factory( p )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a geometric distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.geometric.cdf.factory( 0.5 );\n > var y = mycdf( 3.0 )\n 0.9375\n > y = mycdf( 1.0 )\n 0.75\n\n","base.dists.geometric.cdf.factory":"\nbase.dists.geometric.cdf.factory( p )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a geometric distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.geometric.cdf.factory( 0.5 );\n > var y = mycdf( 3.0 )\n 0.9375\n > y = mycdf( 1.0 )\n 0.75","base.dists.geometric.entropy":"\nbase.dists.geometric.entropy( p )\n Returns the entropy of a geometric distribution with success probability\n `p` (in nats).\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.geometric.entropy( 0.1 )\n ~3.251\n > v = base.dists.geometric.entropy( 0.5 )\n ~1.386\n\n","base.dists.geometric.Geometric":"\nbase.dists.geometric.Geometric( [p] )\n Returns a geometric distribution object.\n\n Parameters\n ----------\n p: number (optional)\n Success probability. Must be between `0` and `1`. Default: `0.5`.\n\n Returns\n -------\n geometric: Object\n Distribution instance.\n\n geometric.p: number\n Success probability. If set, the value must be between `0` and `1`.\n\n geometric.entropy: number\n Read-only property which returns the differential entropy.\n\n geometric.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n geometric.mean: number\n Read-only property which returns the expected value.\n\n geometric.median: number\n Read-only property which returns the median.\n\n geometric.mode: number\n Read-only property which returns the mode.\n\n geometric.skewness: number\n Read-only property which returns the skewness.\n\n geometric.stdev: number\n Read-only property which returns the standard deviation.\n\n geometric.variance: number\n Read-only property which returns the variance.\n\n geometric.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n geometric.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n geometric.logpmf: Function\n Evaluates the natural logarithm of the probability mass function (PMF).\n\n geometric.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n geometric.pmf: Function\n Evaluates the probability mass function (PMF).\n\n geometric.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var geometric = base.dists.geometric.Geometric( 0.6 );\n > geometric.p\n 0.6\n > geometric.entropy\n ~1.122\n > geometric.kurtosis\n ~6.9\n > geometric.mean\n ~0.667\n > geometric.median\n 0.0\n > geometric.mode\n 0.0\n > geometric.skewness\n ~2.214\n > geometric.stdev\n ~1.054\n > geometric.variance\n ~1.111\n > geometric.cdf( 3.0 )\n ~0.974\n > geometric.logcdf( 3.0 )\n ~-0.026\n > geometric.logpmf( 4.0 )\n ~-4.176\n > geometric.mgf( 0.5 )\n ~2.905\n > geometric.pmf( 2.0 )\n ~0.096\n > geometric.quantile( 0.7 )\n 1.0\n\n","base.dists.geometric.kurtosis":"\nbase.dists.geometric.kurtosis( p )\n Returns the excess kurtosis of a geometric distribution with success\n probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.geometric.kurtosis( 0.1 )\n ~6.011\n > v = base.dists.geometric.kurtosis( 0.5 )\n 6.5\n\n","base.dists.geometric.logcdf":"\nbase.dists.geometric.logcdf( x, p )\n Evaluates the logarithm of the cumulative distribution function (CDF) for a\n geometric distribution with success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.geometric.logcdf( 2.0, 0.5 )\n ~-0.134\n > y = base.dists.geometric.logcdf( 2.0, 0.1 )\n ~-1.306\n > y = base.dists.geometric.logcdf( -1.0, 4.0 )\n -Infinity\n > y = base.dists.geometric.logcdf( NaN, 0.5 )\n NaN\n > y = base.dists.geometric.logcdf( 0.0, NaN )\n NaN\n // Invalid probability\n > y = base.dists.geometric.logcdf( 2.0, 1.4 )\n NaN\n\n\nbase.dists.geometric.logcdf.factory( p )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a geometric distribution with success\n probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.geometric.logcdf.factory( 0.5 );\n > var y = mylogcdf( 3.0 )\n ~-0.065\n > y = mylogcdf( 1.0 )\n ~-0.288\n\n","base.dists.geometric.logcdf.factory":"\nbase.dists.geometric.logcdf.factory( p )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a geometric distribution with success\n probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.geometric.logcdf.factory( 0.5 );\n > var y = mylogcdf( 3.0 )\n ~-0.065\n > y = mylogcdf( 1.0 )\n ~-0.288","base.dists.geometric.logpmf":"\nbase.dists.geometric.logpmf( x, p )\n Evaluates the logarithm of the probability mass function (PMF) for a\n geometric distribution with success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated logPMF.\n\n Examples\n --------\n > var y = base.dists.geometric.logpmf( 4.0, 0.3 )\n ~-2.631\n > y = base.dists.geometric.logpmf( 2.0, 0.7 )\n ~-2.765\n > y = base.dists.geometric.logpmf( -1.0, 0.5 )\n -Infinity\n > y = base.dists.geometric.logpmf( 0.0, NaN )\n NaN\n > y = base.dists.geometric.logpmf( NaN, 0.5 )\n NaN\n // Invalid success probability:\n > y = base.dists.geometric.logpmf( 2.0, 1.5 )\n NaN\n\n\nbase.dists.geometric.logpmf.factory( p )\n Returns a function for evaluating the logarithm of the probability mass\n function (PMF) of a geometric distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogpmf = base.dists.geometric.logpmf.factory( 0.5 );\n > var y = mylogpmf( 3.0 )\n ~-2.773\n > y = mylogpmf( 1.0 )\n ~-1.386\n\n","base.dists.geometric.logpmf.factory":"\nbase.dists.geometric.logpmf.factory( p )\n Returns a function for evaluating the logarithm of the probability mass\n function (PMF) of a geometric distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogpmf = base.dists.geometric.logpmf.factory( 0.5 );\n > var y = mylogpmf( 3.0 )\n ~-2.773\n > y = mylogpmf( 1.0 )\n ~-1.386","base.dists.geometric.mean":"\nbase.dists.geometric.mean( p )\n Returns the expected value of a geometric distribution with success\n probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.geometric.mean( 0.1 )\n 9.0\n > v = base.dists.geometric.mean( 0.5 )\n 1.0\n\n","base.dists.geometric.median":"\nbase.dists.geometric.median( p )\n Returns the median of a geometric distribution with success probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: integer\n Median.\n\n Examples\n --------\n > var v = base.dists.geometric.median( 0.1 )\n 6\n > v = base.dists.geometric.median( 0.5 )\n 0\n\n","base.dists.geometric.mgf":"\nbase.dists.geometric.mgf( t, p )\n Evaluates the moment-generating function (MGF) for a geometric\n distribution with success probability `p` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If `t >= -ln(1-p)`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.geometric.mgf( 0.2, 0.5 )\n ~1.569\n > y = base.dists.geometric.mgf( 0.4, 0.5 )\n ~2.936\n // Case: t >= -ln(1-p)\n > y = base.dists.geometric.mgf( 0.8, 0.5 )\n NaN\n > y = base.dists.geometric.mgf( NaN, 0.0 )\n NaN\n > y = base.dists.geometric.mgf( 0.0, NaN )\n NaN\n > y = base.dists.geometric.mgf( -2.0, -1.0 )\n NaN\n > y = base.dists.geometric.mgf( 0.2, 2.0 )\n NaN\n\n\nbase.dists.geometric.mgf.factory( p )\n Returns a function for evaluating the moment-generating function (MGF) of a\n geometric distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.geometric.mgf.factory( 0.8 );\n > var y = mymgf( -0.2 )\n ~0.783\n\n","base.dists.geometric.mgf.factory":"\nbase.dists.geometric.mgf.factory( p )\n Returns a function for evaluating the moment-generating function (MGF) of a\n geometric distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.geometric.mgf.factory( 0.8 );\n > var y = mymgf( -0.2 )\n ~0.783","base.dists.geometric.mode":"\nbase.dists.geometric.mode( p )\n Returns the mode of a geometric distribution with success probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: integer\n Mode.\n\n Examples\n --------\n > var v = base.dists.geometric.mode( 0.1 )\n 0\n > v = base.dists.geometric.mode( 0.5 )\n 0\n\n","base.dists.geometric.pmf":"\nbase.dists.geometric.pmf( x, p )\n Evaluates the probability mass function (PMF) for a geometric distribution\n with success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated PMF.\n\n Examples\n --------\n > var y = base.dists.geometric.pmf( 4.0, 0.3 )\n ~0.072\n > y = base.dists.geometric.pmf( 2.0, 0.7 )\n ~0.063\n > y = base.dists.geometric.pmf( -1.0, 0.5 )\n 0.0\n > y = base.dists.geometric.pmf( 0.0, NaN )\n NaN\n > y = base.dists.geometric.pmf( NaN, 0.5 )\n NaN\n // Invalid success probability:\n > y = base.dists.geometric.pmf( 2.0, 1.5 )\n NaN\n\n\nbase.dists.geometric.pmf.factory( p )\n Returns a function for evaluating the probability mass function (PMF) of a\n geometric distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var mypmf = base.dists.geometric.pmf.factory( 0.5 );\n > var y = mypmf( 3.0 )\n 0.0625\n > y = mypmf( 1.0 )\n 0.25\n\n","base.dists.geometric.pmf.factory":"\nbase.dists.geometric.pmf.factory( p )\n Returns a function for evaluating the probability mass function (PMF) of a\n geometric distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var mypmf = base.dists.geometric.pmf.factory( 0.5 );\n > var y = mypmf( 3.0 )\n 0.0625\n > y = mypmf( 1.0 )\n 0.25","base.dists.geometric.quantile":"\nbase.dists.geometric.quantile( r, p )\n Evaluates the quantile function for a geometric distribution with success\n probability `p` at a probability `r`.\n\n If `r < 0` or `r > 1`, the function returns `NaN`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n r: number\n Input probability.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.geometric.quantile( 0.8, 0.4 )\n 3\n > y = base.dists.geometric.quantile( 0.5, 0.4 )\n 1\n > y = base.dists.geometric.quantile( 0.9, 0.1 )\n 21\n\n > y = base.dists.geometric.quantile( -0.2, 0.1 )\n NaN\n\n > y = base.dists.geometric.quantile( NaN, 0.8 )\n NaN\n > y = base.dists.geometric.quantile( 0.4, NaN )\n NaN\n\n > y = base.dists.geometric.quantile( 0.5, -1.0 )\n NaN\n > y = base.dists.geometric.quantile( 0.5, 1.5 )\n NaN\n\n\nbase.dists.geometric.quantile.factory( p )\n Returns a function for evaluating the quantile function of a geometric\n distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.geometric.quantile.factory( 0.4 );\n > var y = myquantile( 0.4 )\n 0\n > y = myquantile( 0.8 )\n 3\n > y = myquantile( 1.0 )\n Infinity\n\n","base.dists.geometric.quantile.factory":"\nbase.dists.geometric.quantile.factory( p )\n Returns a function for evaluating the quantile function of a geometric\n distribution with success probability `p`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.geometric.quantile.factory( 0.4 );\n > var y = myquantile( 0.4 )\n 0\n > y = myquantile( 0.8 )\n 3\n > y = myquantile( 1.0 )\n Infinity","base.dists.geometric.skewness":"\nbase.dists.geometric.skewness( p )\n Returns the skewness of a geometric distribution with success probability\n `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.geometric.skewness( 0.1 )\n ~2.003\n > v = base.dists.geometric.skewness( 0.5 )\n ~2.121\n\n","base.dists.geometric.stdev":"\nbase.dists.geometric.stdev( p )\n Returns the standard deviation of a geometric distribution with success\n probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.geometric.stdev( 0.1 )\n ~9.487\n > v = base.dists.geometric.stdev( 0.5 )\n ~1.414\n\n","base.dists.geometric.variance":"\nbase.dists.geometric.variance( p )\n Returns the variance of a geometric distribution with success probability\n `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.geometric.variance( 0.1 )\n ~90.0\n > v = base.dists.geometric.variance( 0.5 )\n 2.0\n\n","base.dists.gumbel.cdf":"\nbase.dists.gumbel.cdf( x, μ, β )\n Evaluates the cumulative distribution function (CDF) for a Gumbel\n distribution with location parameter `μ` and scale parameter `β` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.gumbel.cdf( 10.0, 0.0, 3.0 )\n ~0.965\n > y = base.dists.gumbel.cdf( -2.0, 0.0, 3.0 )\n ~0.143\n > y = base.dists.gumbel.cdf( 0.0, 0.0, 1.0 )\n ~0.368\n > y = base.dists.gumbel.cdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.gumbel.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.cdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.gumbel.cdf( 0.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.gumbel.cdf.factory( μ, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Gumbel distribution with location parameter `μ` and scale parameter\n `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.gumbel.cdf.factory( 2.0, 3.0 );\n > var y = myCDF( 10.0 )\n ~0.933\n > y = myCDF( 2.0 )\n ~0.368\n\n","base.dists.gumbel.cdf.factory":"\nbase.dists.gumbel.cdf.factory( μ, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Gumbel distribution with location parameter `μ` and scale parameter\n `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.gumbel.cdf.factory( 2.0, 3.0 );\n > var y = myCDF( 10.0 )\n ~0.933\n > y = myCDF( 2.0 )\n ~0.368","base.dists.gumbel.entropy":"\nbase.dists.gumbel.entropy( μ, β )\n Returns the differential entropy of a Gumbel distribution with location\n parameter `μ` and scale parameter `β` (in nats).\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var y = base.dists.gumbel.entropy( 0.0, 1.0 )\n ~1.577\n > y = base.dists.gumbel.entropy( 4.0, 2.0 )\n ~2.27\n > y = base.dists.gumbel.entropy( NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.entropy( 0.0, NaN )\n NaN\n > y = base.dists.gumbel.entropy( 0.0, 0.0 )\n NaN\n\n","base.dists.gumbel.Gumbel":"\nbase.dists.gumbel.Gumbel( [μ, β] )\n Returns a Gumbel distribution object.\n\n Parameters\n ----------\n μ: number (optional)\n Location parameter. Default: `0.0`.\n\n β: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n gumbel: Object\n Distribution instance.\n\n gumbel.mu: number\n Location parameter.\n\n gumbel.beta: number\n Scale parameter. If set, the value must be greater than `0`.\n\n gumbel.entropy: number\n Read-only property which returns the differential entropy.\n\n gumbel.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n gumbel.mean: number\n Read-only property which returns the expected value.\n\n gumbel.median: number\n Read-only property which returns the median.\n\n gumbel.mode: number\n Read-only property which returns the mode.\n\n gumbel.skewness: number\n Read-only property which returns the skewness.\n\n gumbel.stdev: number\n Read-only property which returns the standard deviation.\n\n gumbel.variance: number\n Read-only property which returns the variance.\n\n gumbel.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n gumbel.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n gumbel.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n gumbel.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n gumbel.pdf: Function\n Evaluates the probability density function (PDF).\n\n gumbel.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var gumbel = base.dists.gumbel.Gumbel( -2.0, 3.0 );\n > gumbel.mu\n -2.0\n > gumbel.beta\n 3.0\n > gumbel.entropy\n ~2.676\n > gumbel.kurtosis\n 2.4\n > gumbel.mean\n ~-0.268\n > gumbel.median\n ~-0.9\n > gumbel.mode\n -2.0\n > gumbel.skewness\n ~1.14\n > gumbel.stdev\n ~3.848\n > gumbel.variance\n ~14.804\n > gumbel.cdf( 0.8 )\n ~0.675\n > gumbel.logcdf( 0.8 )\n ~-0.393\n > gumbel.logpdf( 1.0 )\n ~-2.466\n > gumbel.mgf( 0.2 )\n ~1.487\n > gumbel.pdf( 1.0 )\n ~0.085\n > gumbel.quantile( 0.8 )\n ~2.5\n\n","base.dists.gumbel.kurtosis":"\nbase.dists.gumbel.kurtosis( μ, β )\n Returns the excess kurtosis of a Gumbel distribution with location parameter\n `μ` and scale parameter `β`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var y = base.dists.gumbel.kurtosis( 0.0, 1.0 )\n 2.4\n > y = base.dists.gumbel.kurtosis( 4.0, 2.0 )\n 2.4\n > y = base.dists.gumbel.kurtosis( NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.kurtosis( 0.0, NaN )\n NaN\n > y = base.dists.gumbel.kurtosis( 0.0, 0.0 )\n NaN\n\n","base.dists.gumbel.logcdf":"\nbase.dists.gumbel.logcdf( x, μ, β )\n Evaluates the logarithm of the cumulative distribution function (CDF) for a\n Gumbel distribution with location parameter `μ` and scale parameter `β` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.gumbel.logcdf( 10.0, 0.0, 3.0 )\n ~-0.036\n > y = base.dists.gumbel.logcdf( -2.0, 0.0, 3.0 )\n ~-1.948\n > y = base.dists.gumbel.logcdf( 0.0, 0.0, 1.0 )\n ~-1.0\n > y = base.dists.gumbel.logcdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.gumbel.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.logcdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.gumbel.logcdf( 0.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.gumbel.logcdf.factory( μ, β )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Gumbel distribution with location parameter\n `μ` and scale parameter `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var myLCDF = base.dists.gumbel.logcdf.factory( 2.0, 3.0 );\n > var y = myLCDF( 10.0 )\n ~-0.069\n > y = myLCDF( 2.0 )\n ~-1.0\n\n","base.dists.gumbel.logcdf.factory":"\nbase.dists.gumbel.logcdf.factory( μ, β )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Gumbel distribution with location parameter\n `μ` and scale parameter `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var myLCDF = base.dists.gumbel.logcdf.factory( 2.0, 3.0 );\n > var y = myLCDF( 10.0 )\n ~-0.069\n > y = myLCDF( 2.0 )\n ~-1.0","base.dists.gumbel.logpdf":"\nbase.dists.gumbel.logpdf( x, μ, β )\n Evaluates the logarithm of the probability density function (PDF) for a\n Gumbel distribution with location parameter `μ` and scale parameter `β` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.gumbel.logpdf( 0.0, 0.0, 2.0 )\n ~-1.693\n > y = base.dists.gumbel.logpdf( 0.0, 0.0, 1.0 )\n ~-1\n > y = base.dists.gumbel.logpdf( 1.0, 3.0, 2.0 )\n ~-2.411\n > y = base.dists.gumbel.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.gumbel.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.logpdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.gumbel.logpdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.gumbel.logpdf.factory( μ, β )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Gumbel distribution with location parameter `μ` and\n scale parameter `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.gumbel.logpdf.factory( 10.0, 2.0 );\n > var y = mylogpdf( 10.0 )\n ~-1.693\n > y = mylogpdf( 12.0 )\n ~-2.061\n\n","base.dists.gumbel.logpdf.factory":"\nbase.dists.gumbel.logpdf.factory( μ, β )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Gumbel distribution with location parameter `μ` and\n scale parameter `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.gumbel.logpdf.factory( 10.0, 2.0 );\n > var y = mylogpdf( 10.0 )\n ~-1.693\n > y = mylogpdf( 12.0 )\n ~-2.061","base.dists.gumbel.mean":"\nbase.dists.gumbel.mean( μ, β )\n Returns the expected value of a Gumbel distribution with location parameter\n `μ` and scale parameter `β`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var y = base.dists.gumbel.mean( 0.0, 1.0 )\n ~0.577\n > y = base.dists.gumbel.mean( 4.0, 2.0 )\n ~5.154\n > y = base.dists.gumbel.mean( NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.mean( 0.0, NaN )\n NaN\n > y = base.dists.gumbel.mean( 0.0, 0.0 )\n NaN\n\n","base.dists.gumbel.median":"\nbase.dists.gumbel.median( μ, β )\n Returns the median of a Gumbel distribution with location parameter `μ` and\n scale parameter `β`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var y = base.dists.gumbel.median( 0.0, 1.0 )\n ~0.367\n > y = base.dists.gumbel.median( 4.0, 2.0 )\n ~4.733\n > y = base.dists.gumbel.median( NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.median( 0.0, NaN )\n NaN\n > y = base.dists.gumbel.median( 0.0, 0.0 )\n NaN\n\n","base.dists.gumbel.mgf":"\nbase.dists.gumbel.mgf( t, μ, β )\n Evaluates the moment-generating function (MGF) for a Gumbel distribution\n with location parameter `μ` and scale parameter `β` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.gumbel.mgf( -1.0, 0.0, 3.0 )\n 6.0\n > y = base.dists.gumbel.mgf( 0.0, 0.0, 1.0 )\n 1.0\n > y = base.dists.gumbel.mgf( 0.1, 0.0, 3.0 )\n ~1.298\n\n > y = base.dists.gumbel.mgf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.gumbel.mgf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.mgf( 0.0, 0.0, NaN )\n NaN\n\n // Case: `t >= 1/beta`\n > y = base.dists.gumbel.mgf( 0.8, 0.0, 2.0 )\n NaN\n\n // Non-positive scale parameter:\n > y = base.dists.gumbel.mgf( 0.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.gumbel.mgf.factory( μ, β )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Gumbel distribution with location parameter `μ` and scale parameter `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.gumbel.mgf.factory( 0.0, 3.0 );\n > var y = myMGF( -1.5 )\n ~52.343\n > y = myMGF( -1.0 )\n 6.0\n\n","base.dists.gumbel.mgf.factory":"\nbase.dists.gumbel.mgf.factory( μ, β )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Gumbel distribution with location parameter `μ` and scale parameter `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.gumbel.mgf.factory( 0.0, 3.0 );\n > var y = myMGF( -1.5 )\n ~52.343\n > y = myMGF( -1.0 )\n 6.0","base.dists.gumbel.mode":"\nbase.dists.gumbel.mode( μ, β )\n Returns the mode of a Gumbel distribution with location parameter `μ` and\n scale parameter `β`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var y = base.dists.gumbel.mode( 0.0, 1.0 )\n 0.0\n > y = base.dists.gumbel.mode( 4.0, 2.0 )\n 4.0\n > y = base.dists.gumbel.mode( NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.mode( 0.0, NaN )\n NaN\n > y = base.dists.gumbel.mode( 0.0, 0.0 )\n NaN\n\n","base.dists.gumbel.pdf":"\nbase.dists.gumbel.pdf( x, μ, β )\n Evaluates the probability density function (PDF) for a Gumbel distribution\n with location parameter `μ` and scale parameter `β` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.gumbel.pdf( 0.0, 0.0, 2.0 )\n ~0.184\n > y = base.dists.gumbel.pdf( 0.0, 0.0, 1.0 )\n ~0.368\n > y = base.dists.gumbel.pdf( 1.0, 3.0, 2.0 )\n ~0.09\n > y = base.dists.gumbel.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.gumbel.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.pdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.gumbel.pdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.gumbel.pdf.factory( μ, β )\n Returns a function for evaluating the probability density function (PDF)\n of a Gumbel distribution with location parameter `μ` and scale parameter\n `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.gumbel.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n ~0.184\n > y = myPDF( 12.0 )\n ~0.127\n\n","base.dists.gumbel.pdf.factory":"\nbase.dists.gumbel.pdf.factory( μ, β )\n Returns a function for evaluating the probability density function (PDF)\n of a Gumbel distribution with location parameter `μ` and scale parameter\n `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.gumbel.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n ~0.184\n > y = myPDF( 12.0 )\n ~0.127","base.dists.gumbel.quantile":"\nbase.dists.gumbel.quantile( p, μ, β )\n Evaluates the quantile function for a Gumbel distribution with location\n parameter `μ` and scale parameter `β` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.gumbel.quantile( 0.8, 0.0, 1.0 )\n ~1.5\n > y = base.dists.gumbel.quantile( 0.5, 4.0, 2.0 )\n ~4.733\n > y = base.dists.gumbel.quantile( 0.5, 4.0, 4.0 )\n ~5.466\n\n > y = base.dists.gumbel.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.gumbel.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.gumbel.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.gumbel.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.quantile( 0.0, 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.gumbel.quantile( 0.5, 0.0, -1.0 )\n NaN\n\n\nbase.dists.gumbel.quantile.factory( μ, β )\n Returns a function for evaluating the quantile function of a Gumbel\n distribution with location parameter `μ` and scale parameter `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.gumbel.quantile.factory( 8.0, 2.0 );\n > var y = myQuantile( 0.5 )\n ~8.733\n > y = myQuantile( 0.7 )\n ~10.062\n\n","base.dists.gumbel.quantile.factory":"\nbase.dists.gumbel.quantile.factory( μ, β )\n Returns a function for evaluating the quantile function of a Gumbel\n distribution with location parameter `μ` and scale parameter `β`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.gumbel.quantile.factory( 8.0, 2.0 );\n > var y = myQuantile( 0.5 )\n ~8.733\n > y = myQuantile( 0.7 )\n ~10.062","base.dists.gumbel.skewness":"\nbase.dists.gumbel.skewness( μ, β )\n Returns the skewness of a Gumbel distribution with location parameter `μ`\n and scale parameter `β`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var y = base.dists.gumbel.skewness( 0.0, 1.0 )\n ~1.14\n > y = base.dists.gumbel.skewness( 4.0, 2.0 )\n ~1.14\n > y = base.dists.gumbel.skewness( NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.skewness( 0.0, NaN )\n NaN\n > y = base.dists.gumbel.skewness( 0.0, 0.0 )\n NaN\n\n","base.dists.gumbel.stdev":"\nbase.dists.gumbel.stdev( μ, β )\n Returns the standard deviation of a Gumbel distribution with location\n parameter `μ` and scale parameter `β`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var y = base.dists.gumbel.stdev( 0.0, 1.0 )\n ~1.283\n > y = base.dists.gumbel.stdev( 4.0, 2.0 )\n ~2.565\n > y = base.dists.gumbel.stdev( NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.stdev( 0.0, NaN )\n NaN\n > y = base.dists.gumbel.stdev( 0.0, 0.0 )\n NaN\n\n","base.dists.gumbel.variance":"\nbase.dists.gumbel.variance( μ, β )\n Returns the variance of a Gumbel distribution with location parameter `μ`\n and scale parameter `β`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var y = base.dists.gumbel.variance( 0.0, 1.0 )\n ~1.645\n > y = base.dists.gumbel.variance( 4.0, 2.0 )\n ~6.58\n > y = base.dists.gumbel.variance( NaN, 1.0 )\n NaN\n > y = base.dists.gumbel.variance( 0.0, NaN )\n NaN\n > y = base.dists.gumbel.variance( 0.0, 0.0 )\n NaN\n\n","base.dists.hypergeometric.cdf":"\nbase.dists.hypergeometric.cdf( x, N, K, n )\n Evaluates the cumulative distribution function (CDF) for a hypergeometric\n distribution with population size `N`, subpopulation size `K`, and number of\n draws `n` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K` or draws `n` which\n is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or subpopulation size `K` exceeds population size\n `N`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.hypergeometric.cdf( 1.0, 8, 4, 2 )\n ~0.786\n > y = base.dists.hypergeometric.cdf( 1.5, 8, 4, 2 )\n ~0.786\n > y = base.dists.hypergeometric.cdf( 2.0, 8, 4, 2 )\n 1.0\n > y = base.dists.hypergeometric.cdf( 0, 8, 4, 2)\n ~0.214\n\n > y = base.dists.hypergeometric.cdf( NaN, 10, 5, 2 )\n NaN\n > y = base.dists.hypergeometric.cdf( 0.0, NaN, 5, 2 )\n NaN\n > y = base.dists.hypergeometric.cdf( 0.0, 10, NaN, 2 )\n NaN\n > y = base.dists.hypergeometric.cdf( 0.0, 10, 5, NaN )\n NaN\n\n > y = base.dists.hypergeometric.cdf( 2.0, 10.5, 5, 2 )\n NaN\n > y = base.dists.hypergeometric.cdf( 2.0, 10, 1.5, 2 )\n NaN\n > y = base.dists.hypergeometric.cdf( 2.0, 10, 5, -2.0 )\n NaN\n > y = base.dists.hypergeometric.cdf( 2.0, 10, 5, 12 )\n NaN\n > y = base.dists.hypergeometric.cdf( 2.0, 8, 3, 9 )\n NaN\n\n\nbase.dists.hypergeometric.cdf.factory( N, K, n )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a hypergeometric distribution with population size `N`, subpopulation\n size `K`, and number of draws `n`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.hypergeometric.cdf.factory( 30, 20, 5 );\n > var y = myCDF( 4.0 )\n ~0.891\n > y = myCDF( 1.0 )\n ~0.031\n\n","base.dists.hypergeometric.cdf.factory":"\nbase.dists.hypergeometric.cdf.factory( N, K, n )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a hypergeometric distribution with population size `N`, subpopulation\n size `K`, and number of draws `n`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.hypergeometric.cdf.factory( 30, 20, 5 );\n > var y = myCDF( 4.0 )\n ~0.891\n > y = myCDF( 1.0 )\n ~0.031","base.dists.hypergeometric.Hypergeometric":"\nbase.dists.hypergeometric.Hypergeometric( [N, K, n] )\n Returns a hypergeometric distribution object.\n\n Parameters\n ----------\n N: integer (optional)\n Population size. Must be a nonnegative integer larger than or equal to\n `K` and `n`.\n\n K: integer (optional)\n Subpopulation size. Must be a nonnegative integer smaller than or equal\n to `N`.\n\n n: integer (optional)\n Number of draws. Must be a nonnegative integer smaller than or equal to\n `N`.\n\n Returns\n -------\n hypergeometric: Object\n Distribution instance.\n\n hypergeometric.N: number\n Population size. If set, the value must be a nonnegative integer larger\n than or equal to `K` and `n`.\n\n hypergeometric.K: number\n Subpopulation size. If set, the value must be a nonnegative integer\n smaller than or equal to `N`.\n\n hypergeometric.n: number\n Number of draws. If set, the value must be a nonnegative integer\n smaller than or equal to `N`.\n\n hypergeometric.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n hypergeometric.mean: number\n Read-only property which returns the expected value.\n\n hypergeometric.mode: number\n Read-only property which returns the mode.\n\n hypergeometric.skewness: number\n Read-only property which returns the skewness.\n\n hypergeometric.stdev: number\n Read-only property which returns the standard deviation.\n\n hypergeometric.variance: number\n Read-only property which returns the variance.\n\n hypergeometric.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n hypergeometric.logpmf: Function\n Evaluates the natural logarithm of the probability mass function (PMF).\n\n hypergeometric.pmf: Function\n Evaluates the probability mass function (PMF).\n\n hypergeometric.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var hypergeometric = base.dists.hypergeometric.Hypergeometric( 100, 70, 20 );\n > hypergeometric.N\n 100.0\n > hypergeometric.K\n 70.0\n > hypergeometric.n\n 20.0\n > hypergeometric.kurtosis\n ~-0.063\n > hypergeometric.mean\n 14.0\n > hypergeometric.mode\n 14.0\n > hypergeometric.skewness\n ~-0.133\n > hypergeometric.stdev\n ~1.842\n > hypergeometric.variance\n ~3.394\n > hypergeometric.cdf( 2.9 )\n ~0.0\n > hypergeometric.logpmf( 10 )\n ~-3.806\n > hypergeometric.pmf( 10 )\n ~0.022\n > hypergeometric.quantile( 0.8 )\n 16.0\n\n","base.dists.hypergeometric.kurtosis":"\nbase.dists.hypergeometric.kurtosis( N, K, n )\n Returns the excess kurtosis of a hypergeometric distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K` or draws `n` which\n is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or subpopulation size `K` exceed population size\n `N`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.hypergeometric.kurtosis( 16, 11, 4 )\n ~-0.326\n > v = base.dists.hypergeometric.kurtosis( 4, 2, 2 )\n 0.0\n\n > v = base.dists.hypergeometric.kurtosis( 10, 5, 12 )\n NaN\n > v = base.dists.hypergeometric.kurtosis( 10.3, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.kurtosis( 10, 5.5, 4 )\n NaN\n > v = base.dists.hypergeometric.kurtosis( 10, 5, 4.5 )\n NaN\n\n > v = base.dists.hypergeometric.kurtosis( NaN, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.kurtosis( 20, NaN, 4 )\n NaN\n > v = base.dists.hypergeometric.kurtosis( 20, 10, NaN )\n NaN\n\n","base.dists.hypergeometric.logpmf":"\nbase.dists.hypergeometric.logpmf( x, N, K, n )\n Evaluates the natural logarithm of the probability mass function (PMF) for a\n hypergeometric distribution with population size `N`, subpopulation size\n `K`, and number of draws `n` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K`, or draws `n`\n which is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or the subpopulation size `K` exceed population\n size `N`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Evaluated logPMF.\n\n Examples\n --------\n > var y = base.dists.hypergeometric.logpmf( 1.0, 8, 4, 2 )\n ~-0.56\n > y = base.dists.hypergeometric.logpmf( 2.0, 8, 4, 2 )\n ~-1.54\n > y = base.dists.hypergeometric.logpmf( 0.0, 8, 4, 2 )\n ~-1.54\n > y = base.dists.hypergeometric.logpmf( 1.5, 8, 4, 2 )\n -Infinity\n\n > y = base.dists.hypergeometric.logpmf( NaN, 10, 5, 2 )\n NaN\n > y = base.dists.hypergeometric.logpmf( 0.0, NaN, 5, 2 )\n NaN\n > y = base.dists.hypergeometric.logpmf( 0.0, 10, NaN, 2 )\n NaN\n > y = base.dists.hypergeometric.logpmf( 0.0, 10, 5, NaN )\n NaN\n\n > y = base.dists.hypergeometric.logpmf( 2.0, 10.5, 5, 2 )\n NaN\n > y = base.dists.hypergeometric.logpmf( 2.0, 5, 1.5, 2 )\n NaN\n > y = base.dists.hypergeometric.logpmf( 2.0, 10, 5, -2.0 )\n NaN\n > y = base.dists.hypergeometric.logpmf( 2.0, 10, 5, 12 )\n NaN\n > y = base.dists.hypergeometric.logpmf( 2.0, 8, 3, 9 )\n NaN\n\n\nbase.dists.hypergeometric.logpmf.factory( N, K, n )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a hypergeometric distribution with population size\n `N`, subpopulation size `K`, and number of draws `n`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogPMF = base.dists.hypergeometric.logpmf.factory( 30, 20, 5 );\n > var y = mylogPMF( 4.0 )\n ~-1.079\n > y = mylogPMF( 1.0 )\n ~-3.524\n\n","base.dists.hypergeometric.logpmf.factory":"\nbase.dists.hypergeometric.logpmf.factory( N, K, n )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a hypergeometric distribution with population size\n `N`, subpopulation size `K`, and number of draws `n`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogPMF = base.dists.hypergeometric.logpmf.factory( 30, 20, 5 );\n > var y = mylogPMF( 4.0 )\n ~-1.079\n > y = mylogPMF( 1.0 )\n ~-3.524","base.dists.hypergeometric.mean":"\nbase.dists.hypergeometric.mean( N, K, n )\n Returns the expected value of a hypergeometric distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K` or draws `n` which\n is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or the subpopulation size `K` exceed population\n size `N`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.hypergeometric.mean( 16, 11, 4 )\n 2.75\n > v = base.dists.hypergeometric.mean( 2, 1, 1 )\n 0.5\n\n > v = base.dists.hypergeometric.mean( 10, 5, 12 )\n NaN\n > v = base.dists.hypergeometric.mean( 10.3, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.mean( 10, 5.5, 4 )\n NaN\n > v = base.dists.hypergeometric.mean( 10, 5, 4.5 )\n NaN\n\n > v = base.dists.hypergeometric.mean( NaN, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.mean( 20, NaN, 4 )\n NaN\n > v = base.dists.hypergeometric.mean( 20, 10, NaN )\n NaN\n\n","base.dists.hypergeometric.mode":"\nbase.dists.hypergeometric.mode( N, K, n )\n Returns the mode of a hypergeometric distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K` or draws `n` which\n is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or the subpopulation size `K` exceed population\n size `N`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.hypergeometric.mode( 16, 11, 4 )\n 3\n > v = base.dists.hypergeometric.mode( 2, 1, 1 )\n 1\n\n > v = base.dists.hypergeometric.mode( 10, 5, 12 )\n NaN\n > v = base.dists.hypergeometric.mode( 10.3, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.mode( 10, 5.5, 4 )\n NaN\n > v = base.dists.hypergeometric.mode( 10, 5, 4.5 )\n NaN\n\n > v = base.dists.hypergeometric.mode( NaN, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.mode( 20, NaN, 4 )\n NaN\n > v = base.dists.hypergeometric.mode( 20, 10, NaN )\n NaN\n\n","base.dists.hypergeometric.pmf":"\nbase.dists.hypergeometric.pmf( x, N, K, n )\n Evaluates the probability mass function (PMF) for a hypergeometric\n distribution with population size `N`, subpopulation size `K`, and number of\n draws `n` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K` or draws `n` which\n is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or the subpopulation size `K` exceed population\n size `N`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Evaluated PMF.\n\n Examples\n --------\n > var y = base.dists.hypergeometric.pmf( 1.0, 8, 4, 2 )\n ~0.571\n > y = base.dists.hypergeometric.pmf( 2.0, 8, 4, 2 )\n ~0.214\n > y = base.dists.hypergeometric.pmf( 0.0, 8, 4, 2 )\n ~0.214\n > y = base.dists.hypergeometric.pmf( 1.5, 8, 4, 2 )\n 0.0\n\n > y = base.dists.hypergeometric.pmf( NaN, 10, 5, 2 )\n NaN\n > y = base.dists.hypergeometric.pmf( 0.0, NaN, 5, 2 )\n NaN\n > y = base.dists.hypergeometric.pmf( 0.0, 10, NaN, 2 )\n NaN\n > y = base.dists.hypergeometric.pmf( 0.0, 10, 5, NaN )\n NaN\n\n > y = base.dists.hypergeometric.pmf( 2.0, 10.5, 5, 2 )\n NaN\n > y = base.dists.hypergeometric.pmf( 2.0, 5, 1.5, 2 )\n NaN\n > y = base.dists.hypergeometric.pmf( 2.0, 10, 5, -2.0 )\n NaN\n > y = base.dists.hypergeometric.pmf( 2.0, 10, 5, 12 )\n NaN\n > y = base.dists.hypergeometric.pmf( 2.0, 8, 3, 9 )\n NaN\n\n\nbase.dists.hypergeometric.pmf.factory( N, K, n )\n Returns a function for evaluating the probability mass function (PMF) of a\n hypergeometric distribution with population size `N`, subpopulation size\n `K`, and number of draws `n`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var myPMF = base.dists.hypergeometric.pmf.factory( 30, 20, 5 );\n > var y = myPMF( 4.0 )\n ~0.34\n > y = myPMF( 1.0 )\n ~0.029\n\n","base.dists.hypergeometric.pmf.factory":"\nbase.dists.hypergeometric.pmf.factory( N, K, n )\n Returns a function for evaluating the probability mass function (PMF) of a\n hypergeometric distribution with population size `N`, subpopulation size\n `K`, and number of draws `n`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var myPMF = base.dists.hypergeometric.pmf.factory( 30, 20, 5 );\n > var y = myPMF( 4.0 )\n ~0.34\n > y = myPMF( 1.0 )\n ~0.029","base.dists.hypergeometric.quantile":"\nbase.dists.hypergeometric.quantile( p, N, K, n )\n Evaluates the quantile function for a hypergeometric distribution with\n population size `N`, subpopulation size `K`, and number of draws `n` at a\n probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K` or draws `n` which\n is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or the subpopulation size `K` exceed population\n size `N`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.hypergeometric.quantile( 0.4, 40, 20, 10 )\n 5\n > y = base.dists.hypergeometric.quantile( 0.8, 60, 40, 20 )\n 15\n > y = base.dists.hypergeometric.quantile( 0.5, 100, 10, 10 )\n 1\n > y = base.dists.hypergeometric.quantile( 0.0, 100, 40, 20 )\n 0\n > y = base.dists.hypergeometric.quantile( 1.0, 100, 40, 20 )\n 20\n\n > y = base.dists.hypergeometric.quantile( NaN, 40, 20, 10 )\n NaN\n > y = base.dists.hypergeometric.quantile( 0.2, NaN, 20, 10 )\n NaN\n > y = base.dists.hypergeometric.quantile( 0.2, 40, NaN, 10 )\n NaN\n > y = base.dists.hypergeometric.quantile( 0.2, 40, 20, NaN )\n NaN\n\n\nbase.dists.hypergeometric.quantile.factory( N, K, n )\n Returns a function for evaluating the quantile function of a hypergeometric\n distribution with population size `N`, subpopulation size `K`, and number of\n draws `n`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.hypergeometric.quantile.factory( 100, 20, 10 );\n > var y = myQuantile( 0.2 )\n 1\n > y = myQuantile( 0.9 )\n 4\n\n","base.dists.hypergeometric.quantile.factory":"\nbase.dists.hypergeometric.quantile.factory( N, K, n )\n Returns a function for evaluating the quantile function of a hypergeometric\n distribution with population size `N`, subpopulation size `K`, and number of\n draws `n`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.hypergeometric.quantile.factory( 100, 20, 10 );\n > var y = myQuantile( 0.2 )\n 1\n > y = myQuantile( 0.9 )\n 4","base.dists.hypergeometric.skewness":"\nbase.dists.hypergeometric.skewness( N, K, n )\n Returns the skewness of a hypergeometric distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K` or draws `n` which\n is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or the subpopulation size `K` exceed population\n size `N`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.hypergeometric.skewness( 16, 11, 4 )\n ~-0.258\n > v = base.dists.hypergeometric.skewness( 4, 2, 2 )\n 0.0\n\n > v = base.dists.hypergeometric.skewness( 10, 5, 12 )\n NaN\n > v = base.dists.hypergeometric.skewness( 10.3, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.skewness( 10, 5.5, 4 )\n NaN\n > v = base.dists.hypergeometric.skewness( 10, 5, 4.5 )\n NaN\n\n > v = base.dists.hypergeometric.skewness( NaN, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.skewness( 20, NaN, 4 )\n NaN\n > v = base.dists.hypergeometric.skewness( 20, 10, NaN )\n NaN\n\n","base.dists.hypergeometric.stdev":"\nbase.dists.hypergeometric.stdev( N, K, n )\n Returns the standard deviation of a hypergeometric distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K` or draws `n` which\n is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or the subpopulation size `K` exceed population\n size `N`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.hypergeometric.stdev( 16, 11, 4 )\n ~0.829\n > v = base.dists.hypergeometric.stdev( 2, 1, 1 )\n 0.5\n\n > v = base.dists.hypergeometric.stdev( 10, 5, 12 )\n NaN\n > v = base.dists.hypergeometric.stdev( 10.3, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.stdev( 10, 5.5, 4 )\n NaN\n > v = base.dists.hypergeometric.stdev( 10, 5, 4.5 )\n NaN\n\n > v = base.dists.hypergeometric.stdev( NaN, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.stdev( 20, NaN, 4 )\n NaN\n > v = base.dists.hypergeometric.stdev( 20, 10, NaN )\n NaN\n\n","base.dists.hypergeometric.variance":"\nbase.dists.hypergeometric.variance( N, K, n )\n Returns the variance of a hypergeometric distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a population size `N`, subpopulation size `K` or draws `n` which\n is not a nonnegative integer, the function returns `NaN`.\n\n If the number of draws `n` or the subpopulation size `K` exceed population\n size `N`, the function returns `NaN`.\n\n Parameters\n ----------\n N: integer\n Population size.\n\n K: integer\n Subpopulation size.\n\n n: integer\n Number of draws.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.hypergeometric.variance( 16, 11, 4 )\n ~0.688\n > v = base.dists.hypergeometric.variance( 2, 1, 1 )\n 0.25\n\n > v = base.dists.hypergeometric.variance( 10, 5, 12 )\n NaN\n > v = base.dists.hypergeometric.variance( 10.3, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.variance( 10, 5.5, 4 )\n NaN\n > v = base.dists.hypergeometric.variance( 10, 5, 4.5 )\n NaN\n\n > v = base.dists.hypergeometric.variance( NaN, 10, 4 )\n NaN\n > v = base.dists.hypergeometric.variance( 20, NaN, 4 )\n NaN\n > v = base.dists.hypergeometric.variance( 20, 10, NaN )\n NaN\n\n","base.dists.invgamma.cdf":"\nbase.dists.invgamma.cdf( x, α, β )\n Evaluates the cumulative distribution function (CDF) for an inverse gamma\n distribution with shape parameter `α` and scale parameter `β` at a value\n `x`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.invgamma.cdf( 2.0, 1.0, 1.0 )\n ~0.607\n > y = base.dists.invgamma.cdf( 2.0, 3.0, 1.0 )\n ~0.986\n > y = base.dists.invgamma.cdf( -1.0, 2.0, 2.0 )\n 0.0\n > y = base.dists.invgamma.cdf( PINF, 4.0, 2.0 )\n 1.0\n > y = base.dists.invgamma.cdf( NINF, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.invgamma.cdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.invgamma.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.invgamma.cdf( 0.0, 0.0, NaN )\n NaN\n\n > y = base.dists.invgamma.cdf( 2.0, -1.0, 1.0 )\n NaN\n > y = base.dists.invgamma.cdf( 2.0, 1.0, -1.0 )\n NaN\n\n\nbase.dists.invgamma.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of an inverse gamma distribution with shape parameter `α` and scale\n parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.invgamma.cdf.factory( 2.0, 0.5 );\n > var y = myCDF( 0.5 )\n ~0.736\n > y = myCDF( 2.0 )\n ~0.974\n\n","base.dists.invgamma.cdf.factory":"\nbase.dists.invgamma.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of an inverse gamma distribution with shape parameter `α` and scale\n parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.invgamma.cdf.factory( 2.0, 0.5 );\n > var y = myCDF( 0.5 )\n ~0.736\n > y = myCDF( 2.0 )\n ~0.974","base.dists.invgamma.entropy":"\nbase.dists.invgamma.entropy( α, β )\n Returns the differential entropy of an inverse gamma distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.invgamma.entropy( 1.0, 1.0 )\n ~2.154\n > v = base.dists.invgamma.entropy( 4.0, 12.0 )\n ~1.996\n > v = base.dists.invgamma.entropy( 8.0, 2.0 )\n ~-0.922\n\n","base.dists.invgamma.InvGamma":"\nbase.dists.invgamma.InvGamma( [α, β] )\n Returns an inverse gamma distribution object.\n\n Parameters\n ----------\n α: number (optional)\n Shape parameter. Must be greater than `0`. Default: `1.0`.\n\n β: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n invgamma: Object\n Distribution instance.\n\n invgamma.alpha: number\n Shape parameter. If set, the value must be greater than `0`.\n\n invgamma.beta: number\n Scale parameter. If set, the value must be greater than `0`.\n\n invgamma.entropy: number\n Read-only property which returns the differential entropy.\n\n invgamma.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n invgamma.mean: number\n Read-only property which returns the expected value.\n\n invgamma.mode: number\n Read-only property which returns the mode.\n\n invgamma.skewness: number\n Read-only property which returns the skewness.\n\n invgamma.stdev: number\n Read-only property which returns the standard deviation.\n\n invgamma.variance: number\n Read-only property which returns the variance.\n\n invgamma.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n invgamma.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n invgamma.pdf: Function\n Evaluates the probability density function (PDF).\n\n invgamma.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var invgamma = base.dists.invgamma.InvGamma( 6.0, 5.0 );\n > invgamma.alpha\n 6.0\n > invgamma.beta\n 5.0\n > invgamma.entropy\n ~0.454\n > invgamma.kurtosis\n 19.0\n > invgamma.mean\n 1.0\n > invgamma.mode\n ~0.714\n > invgamma.skewness\n ~2.667\n > invgamma.stdev\n 0.5\n > invgamma.variance\n 0.25\n > invgamma.cdf( 0.8 )\n ~0.406\n > invgamma.pdf( 1.0 )\n ~0.877\n > invgamma.logpdf( 1.0 )\n ~-0.131\n > invgamma.quantile( 0.8 )\n ~1.281\n\n","base.dists.invgamma.kurtosis":"\nbase.dists.invgamma.kurtosis( α, β )\n Returns the excess kurtosis of an inverse gamma distribution.\n\n If `α <= 4` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.invgamma.kurtosis( 7.0, 5.0 )\n 12.0\n > v = base.dists.invgamma.kurtosis( 6.0, 12.0 )\n 19.0\n > v = base.dists.invgamma.kurtosis( 8.0, 2.0 )\n ~8.7\n\n","base.dists.invgamma.logpdf":"\nbase.dists.invgamma.logpdf( x, α, β )\n Evaluates the natural logarithm of the probability density function (PDF)\n for an inverse gamma distribution with shape parameter `α` and scale\n parameter `β` at a value `x`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.invgamma.logpdf( 2.0, 0.5, 1.0 )\n ~-2.112\n > y = base.dists.invgamma.logpdf( 0.2, 1.0, 1.0 )\n ~-1.781\n > y = base.dists.invgamma.logpdf( -1.0, 4.0, 2.0 )\n -Infinity\n\n > y = base.dists.invgamma.logpdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.invgamma.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.invgamma.logpdf( 0.0, 1.0, NaN )\n NaN\n\n // Negative shape parameter:\n > y = base.dists.invgamma.logpdf( 2.0, -1.0, 1.0 )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.invgamma.logpdf( 2.0, 1.0, -1.0 )\n NaN\n\n\nbase.dists.invgamma.logpdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) for an inverse gamma distribution with shape\n parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.invgamma.logpdf.factory( 6.0, 7.0 );\n > var y = mylogPDF( 2.0 )\n ~-1.464\n\n","base.dists.invgamma.logpdf.factory":"\nbase.dists.invgamma.logpdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) for an inverse gamma distribution with shape\n parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.invgamma.logpdf.factory( 6.0, 7.0 );\n > var y = mylogPDF( 2.0 )\n ~-1.464","base.dists.invgamma.mean":"\nbase.dists.invgamma.mean( α, β )\n Returns the expected value of an inverse gamma distribution.\n\n If `α <= 1` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.invgamma.mean( 4.0, 12.0 )\n 4.0\n > v = base.dists.invgamma.mean( 8.0, 2.0 )\n ~0.286\n\n","base.dists.invgamma.mode":"\nbase.dists.invgamma.mode( α, β )\n Returns the mode of an inverse gamma distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.invgamma.mode( 1.0, 1.0 )\n 0.5\n > v = base.dists.invgamma.mode( 4.0, 12.0 )\n 2.4\n > v = base.dists.invgamma.mode( 8.0, 2.0 )\n ~0.222\n\n","base.dists.invgamma.pdf":"\nbase.dists.invgamma.pdf( x, α, β )\n Evaluates the probability density function (PDF) for an inverse gamma\n distribution with shape parameter `α` and scale parameter `β` at a value\n `x`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.invgamma.pdf( 2.0, 0.5, 1.0 )\n ~0.121\n > y = base.dists.invgamma.pdf( 0.2, 1.0, 1.0 )\n ~0.168\n > y = base.dists.invgamma.pdf( -1.0, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.invgamma.pdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.invgamma.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.invgamma.pdf( 0.0, 1.0, NaN )\n NaN\n\n // Negative shape parameter:\n > y = base.dists.invgamma.pdf( 2.0, -1.0, 1.0 )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.invgamma.pdf( 2.0, 1.0, -1.0 )\n NaN\n\n\nbase.dists.invgamma.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF)\n of an inverse gamma distribution with shape parameter `α` and scale\n parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.invgamma.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 2.0 )\n ~0.231\n\n","base.dists.invgamma.pdf.factory":"\nbase.dists.invgamma.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF)\n of an inverse gamma distribution with shape parameter `α` and scale\n parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.invgamma.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 2.0 )\n ~0.231","base.dists.invgamma.quantile":"\nbase.dists.invgamma.quantile( p, α, β )\n Evaluates the quantile function for an inverse gamma distribution with shape\n parameter `α` and scale parameter `β` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.invgamma.quantile( 0.8, 2.0, 1.0 )\n ~1.213\n > y = base.dists.invgamma.quantile( 0.5, 4.0, 2.0 )\n ~0.545\n > y = base.dists.invgamma.quantile( 1.1, 1.0, 1.0 )\n NaN\n > y = base.dists.invgamma.quantile( -0.2, 1.0, 1.0 )\n NaN\n\n > y = base.dists.invgamma.quantile( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.invgamma.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.invgamma.quantile( 0.0, 1.0, NaN )\n NaN\n\n // Non-positive shape parameter:\n > y = base.dists.invgamma.quantile( 0.5, -1.0, 1.0 )\n NaN\n\n // Non-positive rate parameter:\n > y = base.dists.invgamma.quantile( 0.5, 1.0, -1.0 )\n NaN\n\n\nbase.dists.invgamma.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of an inverse gamma\n distribution with shape parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.invgamma.quantile.factory( 2.0, 2.0 );\n > var y = myQuantile( 0.8 )\n ~2.426\n > y = myQuantile( 0.4 )\n ~0.989\n\n","base.dists.invgamma.quantile.factory":"\nbase.dists.invgamma.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of an inverse gamma\n distribution with shape parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.invgamma.quantile.factory( 2.0, 2.0 );\n > var y = myQuantile( 0.8 )\n ~2.426\n > y = myQuantile( 0.4 )\n ~0.989","base.dists.invgamma.skewness":"\nbase.dists.invgamma.skewness( α, β )\n Returns the skewness of an inverse gamma distribution.\n\n If `α <= 3` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.invgamma.skewness( 4.0, 12.0 )\n ~5.657\n > v = base.dists.invgamma.skewness( 8.0, 2.0 )\n ~1.96\n\n","base.dists.invgamma.stdev":"\nbase.dists.invgamma.stdev( α, β )\n Returns the standard deviation of an inverse gamma distribution.\n\n If `α <= 2` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.invgamma.stdev( 5.0, 7.0 )\n ~1.01\n > v = base.dists.invgamma.stdev( 4.0, 12.0 )\n ~2.828\n > v = base.dists.invgamma.stdev( 8.0, 2.0 )\n ~0.117\n\n","base.dists.invgamma.variance":"\nbase.dists.invgamma.variance( α, β )\n Returns the variance of an inverse gamma distribution.\n\n If `α <= 2` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Rate parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.invgamma.variance( 5.0, 7.0 )\n ~1.021\n > v = base.dists.invgamma.variance( 4.0, 12.0 )\n 8.0\n > v = base.dists.invgamma.variance( 8.0, 2.0 )\n ~0.014\n\n","base.dists.kumaraswamy.cdf":"\nbase.dists.kumaraswamy.cdf( x, a, b )\n Evaluates the cumulative distribution function (CDF) for Kumaraswamy's\n double bounded distribution with first shape parameter `a` and second shape\n parameter `b` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.kumaraswamy.cdf( 0.5, 1.0, 1.0 )\n ~0.5\n > y = base.dists.kumaraswamy.cdf( 0.5, 2.0, 4.0 )\n ~0.684\n > y = base.dists.kumaraswamy.cdf( 0.2, 2.0, 2.0 )\n ~0.078\n > y = base.dists.kumaraswamy.cdf( 0.8, 4.0, 4.0 )\n ~0.878\n > y = base.dists.kumaraswamy.cdf( -0.5, 4.0, 2.0 )\n 0.0\n > y = base.dists.kumaraswamy.cdf( 1.5, 4.0, 2.0 )\n 1.0\n\n > y = base.dists.kumaraswamy.cdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.kumaraswamy.cdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.kumaraswamy.cdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.cdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.kumaraswamy.cdf.factory( a, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Kumaraswamy's double bounded distribution with first shape parameter\n `a` and second shape parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.kumaraswamy.cdf.factory( 0.5, 1.0 );\n > var y = mycdf( 0.8 )\n ~0.894\n > y = mycdf( 0.3 )\n ~0.548\n\n","base.dists.kumaraswamy.cdf.factory":"\nbase.dists.kumaraswamy.cdf.factory( a, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Kumaraswamy's double bounded distribution with first shape parameter\n `a` and second shape parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.kumaraswamy.cdf.factory( 0.5, 1.0 );\n > var y = mycdf( 0.8 )\n ~0.894\n > y = mycdf( 0.3 )\n ~0.548","base.dists.kumaraswamy.Kumaraswamy":"\nbase.dists.kumaraswamy.Kumaraswamy( [a, b] )\n Returns a Kumaraswamy's double bounded distribution object.\n\n Parameters\n ----------\n a: number (optional)\n First shape parameter. Must be greater than `0`. Default: `1.0`.\n\n b: number (optional)\n Second shape parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n kumaraswamy: Object\n Distribution instance.\n\n kumaraswamy.a: number\n First shape parameter. If set, the value must be greater than `0`.\n\n kumaraswamy.b: number\n Second shape parameter. If set, the value must be greater than `0`.\n\n kumaraswamy.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n kumaraswamy.mean: number\n Read-only property which returns the expected value.\n\n kumaraswamy.mode: number\n Read-only property which returns the mode.\n\n kumaraswamy.skewness: number\n Read-only property which returns the skewness.\n\n kumaraswamy.stdev: number\n Read-only property which returns the standard deviation.\n\n kumaraswamy.variance: number\n Read-only property which returns the variance.\n\n kumaraswamy.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n kumaraswamy.pdf: Function\n Evaluates the probability density function (PDF).\n\n kumaraswamy.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var kumaraswamy = base.dists.kumaraswamy.Kumaraswamy( 6.0, 5.0 );\n > kumaraswamy.a\n 6.0\n > kumaraswamy.b\n 5.0\n > kumaraswamy.kurtosis\n ~3.194\n > kumaraswamy.mean\n ~0.696\n > kumaraswamy.mode\n ~0.746\n > kumaraswamy.skewness\n ~-0.605\n > kumaraswamy.stdev\n ~0.126\n > kumaraswamy.variance\n ~0.016\n > kumaraswamy.cdf( 0.8 )\n ~0.781\n > kumaraswamy.pdf( 1.0 )\n ~0.0\n > kumaraswamy.quantile( 0.8 )\n ~0.807\n\n","base.dists.kumaraswamy.kurtosis":"\nbase.dists.kumaraswamy.kurtosis( a, b )\n Returns the excess kurtosis of a Kumaraswamy's double bounded distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.kumaraswamy.kurtosis( 1.0, 1.0 )\n ~1.8\n > v = base.dists.kumaraswamy.kurtosis( 4.0, 12.0 )\n ~2.704\n > v = base.dists.kumaraswamy.kurtosis( 16.0, 8.0 )\n ~4.311\n\n","base.dists.kumaraswamy.logcdf":"\nbase.dists.kumaraswamy.logcdf( x, a, b )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for Kumaraswamy's double bounded distribution with first shape\n parameter `a` and second shape parameter `b` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.kumaraswamy.logcdf( 0.5, 1.0, 1.0 )\n ~-0.693\n > y = base.dists.kumaraswamy.logcdf( 0.5, 2.0, 4.0 )\n ~-0.38\n > y = base.dists.kumaraswamy.logcdf( 0.2, 2.0, 2.0 )\n ~-2.546\n > y = base.dists.kumaraswamy.logcdf( 0.8, 4.0, 4.0 )\n ~-0.13\n > y = base.dists.kumaraswamy.logcdf( -0.5, 4.0, 2.0 )\n -Infinity\n > y = base.dists.kumaraswamy.logcdf( 1.5, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.kumaraswamy.logcdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.kumaraswamy.logcdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.kumaraswamy.logcdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.logcdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.kumaraswamy.logcdf.factory( a, b )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a Kumaraswamy's double bounded distribution\n with first shape parameter `a` and second shape parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.kumaraswamy.logcdf.factory( 0.5, 1.0 );\n > var y = mylogcdf( 0.8 )\n ~-0.112\n > y = mylogcdf( 0.3 )\n ~-0.602\n\n","base.dists.kumaraswamy.logcdf.factory":"\nbase.dists.kumaraswamy.logcdf.factory( a, b )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a Kumaraswamy's double bounded distribution\n with first shape parameter `a` and second shape parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.kumaraswamy.logcdf.factory( 0.5, 1.0 );\n > var y = mylogcdf( 0.8 )\n ~-0.112\n > y = mylogcdf( 0.3 )\n ~-0.602","base.dists.kumaraswamy.logpdf":"\nbase.dists.kumaraswamy.logpdf( x, a, b )\n Evaluates the natural logarithm of the probability density function (PDF)\n for Kumaraswamy's double bounded distribution with first shape parameter `a`\n and second shape parameter `b` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.kumaraswamy.logpdf( 0.5, 1.0, 1.0 )\n 0.0\n > y = base.dists.kumaraswamy.logpdf( 0.5, 2.0, 4.0 )\n ~0.523\n > y = base.dists.kumaraswamy.logpdf( 0.2, 2.0, 2.0 )\n ~-0.264\n > y = base.dists.kumaraswamy.logpdf( 0.8, 4.0, 4.0 )\n ~0.522\n > y = base.dists.kumaraswamy.logpdf( -0.5, 4.0, 2.0 )\n -Infinity\n > y = base.dists.kumaraswamy.logpdf( 1.5, 4.0, 2.0 )\n -Infinity\n\n > y = base.dists.kumaraswamy.logpdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.kumaraswamy.logpdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.kumaraswamy.logpdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.logpdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.kumaraswamy.logpdf.factory( a, b )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a Kumaraswamy's double bounded distribution with\n first shape parameter `a` and second shape parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.kumaraswamy.logpdf.factory( 0.5, 1.0 );\n > var y = mylogpdf( 0.8 )\n ~-0.582\n > y = mylogpdf( 0.3 )\n ~-0.091\n\n","base.dists.kumaraswamy.logpdf.factory":"\nbase.dists.kumaraswamy.logpdf.factory( a, b )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a Kumaraswamy's double bounded distribution with\n first shape parameter `a` and second shape parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.kumaraswamy.logpdf.factory( 0.5, 1.0 );\n > var y = mylogpdf( 0.8 )\n ~-0.582\n > y = mylogpdf( 0.3 )\n ~-0.091","base.dists.kumaraswamy.mean":"\nbase.dists.kumaraswamy.mean( a, b )\n Returns the mean of a Kumaraswamy's double bounded distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Mean.\n\n Examples\n --------\n > var v = base.dists.kumaraswamy.mean( 1.5, 1.5 )\n ~0.512\n > v = base.dists.kumaraswamy.mean( 4.0, 12.0 )\n ~0.481\n > v = base.dists.kumaraswamy.mean( 16.0, 8.0 )\n ~0.846\n\n","base.dists.kumaraswamy.median":"\nbase.dists.kumaraswamy.median( a, b )\n Returns the median of a Kumaraswamy's double bounded distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.kumaraswamy.median( 1.0, 1.0 )\n 0.5\n > v = base.dists.kumaraswamy.median( 4.0, 12.0 )\n ~0.487\n > v = base.dists.kumaraswamy.median( 16.0, 8.0 )\n ~0.856\n\n","base.dists.kumaraswamy.mode":"\nbase.dists.kumaraswamy.mode( a, b )\n Returns the mode of a Kumaraswamy's double bounded distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a < 1`, `b < 1`, or `a = b = 1`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.kumaraswamy.mode( 1.5, 1.5 )\n ~0.543\n > v = base.dists.kumaraswamy.mode( 4.0, 12.0 )\n ~0.503\n > v = base.dists.kumaraswamy.mode( 16.0, 8.0 )\n ~0.875\n\n","base.dists.kumaraswamy.pdf":"\nbase.dists.kumaraswamy.pdf( x, a, b )\n Evaluates the probability density function (PDF) for Kumaraswamy's double\n bounded distribution with first shape parameter `a` and second shape\n parameter `b` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.kumaraswamy.pdf( 0.5, 1.0, 1.0 )\n 1.0\n > y = base.dists.kumaraswamy.pdf( 0.5, 2.0, 4.0 )\n ~1.688\n > y = base.dists.kumaraswamy.pdf( 0.2, 2.0, 2.0 )\n ~0.768\n > y = base.dists.kumaraswamy.pdf( 0.8, 4.0, 4.0 )\n ~1.686\n > y = base.dists.kumaraswamy.pdf( -0.5, 4.0, 2.0 )\n 0.0\n > y = base.dists.kumaraswamy.pdf( 1.5, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.kumaraswamy.pdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.kumaraswamy.pdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.kumaraswamy.pdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.pdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.kumaraswamy.pdf.factory( a, b )\n Returns a function for evaluating the probability density function (PDF)\n of a Kumaraswamy's double bounded distribution with first shape parameter\n `a` and second shape parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var mypdf = base.dists.kumaraswamy.pdf.factory( 0.5, 1.0 );\n > var y = mypdf( 0.8 )\n ~0.559\n > y = mypdf( 0.3 )\n ~0.913\n\n","base.dists.kumaraswamy.pdf.factory":"\nbase.dists.kumaraswamy.pdf.factory( a, b )\n Returns a function for evaluating the probability density function (PDF)\n of a Kumaraswamy's double bounded distribution with first shape parameter\n `a` and second shape parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var mypdf = base.dists.kumaraswamy.pdf.factory( 0.5, 1.0 );\n > var y = mypdf( 0.8 )\n ~0.559\n > y = mypdf( 0.3 )\n ~0.913","base.dists.kumaraswamy.quantile":"\nbase.dists.kumaraswamy.quantile( p, a, b )\n Evaluates the quantile function for a Kumaraswamy's double bounded\n distribution with first shape parameter `a` and second shape parameter `b`\n at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.kumaraswamy.quantile( 0.5, 1.0, 1.0 )\n 0.5\n > y = base.dists.kumaraswamy.quantile( 0.5, 2.0, 4.0 )\n ~0.399\n > y = base.dists.kumaraswamy.quantile( 0.2, 2.0, 2.0 )\n ~0.325\n > y = base.dists.kumaraswamy.quantile( 0.8, 4.0, 4.0 )\n ~0.759\n\n > y = base.dists.kumaraswamy.quantile( -0.5, 4.0, 2.0 )\n NaN\n > y = base.dists.kumaraswamy.quantile( 1.5, 4.0, 2.0 )\n NaN\n\n > y = base.dists.kumaraswamy.quantile( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.kumaraswamy.quantile( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.kumaraswamy.quantile( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.kumaraswamy.quantile( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.kumaraswamy.quantile.factory( a, b )\n Returns a function for evaluating the quantile function of a Kumaraswamy's\n double bounded distribution with first shape parameter `a` and second shape\n parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.kumaraswamy.quantile.factory( 0.5, 1.0 );\n > var y = myQuantile( 0.8 )\n ~0.64\n > y = myQuantile( 0.3 )\n ~0.09\n\n","base.dists.kumaraswamy.quantile.factory":"\nbase.dists.kumaraswamy.quantile.factory( a, b )\n Returns a function for evaluating the quantile function of a Kumaraswamy's\n double bounded distribution with first shape parameter `a` and second shape\n parameter `b`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.kumaraswamy.quantile.factory( 0.5, 1.0 );\n > var y = myQuantile( 0.8 )\n ~0.64\n > y = myQuantile( 0.3 )\n ~0.09","base.dists.kumaraswamy.skewness":"\nbase.dists.kumaraswamy.skewness( a, b )\n Returns the skewness of a Kumaraswamy's double bounded distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.kumaraswamy.skewness( 1.0, 1.0 )\n ~1.154e-15\n > v = base.dists.kumaraswamy.skewness( 4.0, 12.0 )\n ~-0.201\n > v = base.dists.kumaraswamy.skewness( 16.0, 8.0 )\n ~-0.94\n\n","base.dists.kumaraswamy.stdev":"\nbase.dists.kumaraswamy.stdev( a, b )\n Returns the standard deviation of a Kumaraswamy's double bounded\n distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.kumaraswamy.stdev( 1.0, 1.0 )\n ~0.289\n > v = base.dists.kumaraswamy.stdev( 4.0, 12.0 )\n ~0.13\n > v = base.dists.kumaraswamy.stdev( 16.0, 8.0 )\n ~0.062\n\n","base.dists.kumaraswamy.variance":"\nbase.dists.kumaraswamy.variance( a, b )\n Returns the variance of a Kumaraswamy's double bounded distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `a <= 0` or `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n First shape parameter.\n\n b: number\n Second shape parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.kumaraswamy.variance( 1.0, 1.0 )\n ~0.083\n > v = base.dists.kumaraswamy.variance( 4.0, 12.0 )\n ~0.017\n > v = base.dists.kumaraswamy.variance( 16.0, 8.0 )\n ~0.004\n\n","base.dists.laplace.cdf":"\nbase.dists.laplace.cdf( x, μ, b )\n Evaluates the cumulative distribution function (CDF) for a Laplace\n distribution with scale parameter `b` and location parameter `μ` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.laplace.cdf( 2.0, 0.0, 1.0 )\n ~0.932\n > y = base.dists.laplace.cdf( 5.0, 10.0, 3.0 )\n ~0.094\n > y = base.dists.laplace.cdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.laplace.cdf( 2, NaN, 1.0 )\n NaN\n > y = base.dists.laplace.cdf( 2.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.laplace.cdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.laplace.cdf.factory( μ, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Laplace distribution with scale parameter `b` and location parameter\n `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.laplace.cdf.factory( 2.0, 3.0 );\n > var y = myCDF( 10.0 )\n ~0.965\n > y = myCDF( 2.0 )\n 0.5\n\n","base.dists.laplace.cdf.factory":"\nbase.dists.laplace.cdf.factory( μ, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Laplace distribution with scale parameter `b` and location parameter\n `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.laplace.cdf.factory( 2.0, 3.0 );\n > var y = myCDF( 10.0 )\n ~0.965\n > y = myCDF( 2.0 )\n 0.5","base.dists.laplace.entropy":"\nbase.dists.laplace.entropy( μ, b )\n Returns the differential entropy of a Laplace distribution with location\n parameter `μ` and scale parameter `b`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Differential entropy.\n\n Examples\n --------\n > var y = base.dists.laplace.entropy( 0.0, 1.0 )\n ~1.693\n > y = base.dists.laplace.entropy( 4.0, 2.0 )\n ~2.386\n > y = base.dists.laplace.entropy( NaN, 1.0 )\n NaN\n > y = base.dists.laplace.entropy( 0.0, NaN )\n NaN\n > y = base.dists.laplace.entropy( 0.0, 0.0 )\n NaN\n\n","base.dists.laplace.kurtosis":"\nbase.dists.laplace.kurtosis( μ, b )\n Returns the excess kurtosis of a Laplace distribution with location\n parameter `μ` and scale parameter `b`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var y = base.dists.laplace.kurtosis( 0.0, 1.0 )\n 3.0\n > y = base.dists.laplace.kurtosis( 4.0, 2.0 )\n 3.0\n > y = base.dists.laplace.kurtosis( NaN, 1.0 )\n NaN\n > y = base.dists.laplace.kurtosis( 0.0, NaN )\n NaN\n > y = base.dists.laplace.kurtosis( 0.0, 0.0 )\n NaN\n\n","base.dists.laplace.Laplace":"\nbase.dists.laplace.Laplace( [μ, b] )\n Returns a Laplace distribution object.\n\n Parameters\n ----------\n μ: number (optional)\n Location parameter. Default: `0.0`.\n\n b: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n laplace: Object\n Distribution instance.\n\n laplace.mu: number\n Location parameter.\n\n laplace.b: number\n Scale parameter. If set, the value must be greater than `0`.\n\n laplace.entropy: number\n Read-only property which returns the differential entropy.\n\n laplace.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n laplace.mean: number\n Read-only property which returns the expected value.\n\n laplace.median: number\n Read-only property which returns the median.\n\n laplace.mode: number\n Read-only property which returns the mode.\n\n laplace.skewness: number\n Read-only property which returns the skewness.\n\n laplace.stdev: number\n Read-only property which returns the standard deviation.\n\n laplace.variance: number\n Read-only property which returns the variance.\n\n laplace.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n laplace.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n laplace.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n laplace.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n laplace.pdf: Function\n Evaluates the probability density function (PDF).\n\n laplace.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var laplace = base.dists.laplace.Laplace( -2.0, 3.0 );\n > laplace.mu\n -2.0\n > laplace.b\n 3.0\n > laplace.entropy\n ~2.792\n > laplace.kurtosis\n 3.0\n > laplace.mean\n -2.0\n > laplace.median\n -2.0\n > laplace.mode\n -2.0\n > laplace.skewness\n 0.0\n > laplace.stdev\n ~4.243\n > laplace.variance\n 18.0\n > laplace.cdf( 0.8 )\n ~0.803\n > laplace.logcdf( 0.8 )\n ~-0.219\n > laplace.logpdf( 1.0 )\n ~-2.792\n > laplace.mgf( 0.2 )\n ~1.047\n > laplace.pdf( 2.0 )\n ~0.044\n > laplace.quantile( 0.9 )\n ~2.828\n\n","base.dists.laplace.logcdf":"\nbase.dists.laplace.logcdf( x, μ, b )\n Evaluates the logarithm of the cumulative distribution function (CDF) for a\n Laplace distribution with scale parameter `b` and location parameter `μ` at\n a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.laplace.logcdf( 2.0, 0.0, 1.0 )\n ~-0.07\n > y = base.dists.laplace.logcdf( 5.0, 10.0, 3.0 )\n ~-2.36\n > y = base.dists.laplace.logcdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.laplace.logcdf( 2, NaN, 1.0 )\n NaN\n > y = base.dists.laplace.logcdf( 2.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.laplace.logcdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.laplace.logcdf.factory( μ, b )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Laplace distribution with scale parameter\n `b` and location parameter `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.laplace.logcdf.factory( 2.0, 3.0 );\n > var y = mylogcdf( 10.0 )\n ~-0.035\n > y = mylogcdf( 2.0 )\n ~-0.693\n\n","base.dists.laplace.logcdf.factory":"\nbase.dists.laplace.logcdf.factory( μ, b )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Laplace distribution with scale parameter\n `b` and location parameter `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.laplace.logcdf.factory( 2.0, 3.0 );\n > var y = mylogcdf( 10.0 )\n ~-0.035\n > y = mylogcdf( 2.0 )\n ~-0.693","base.dists.laplace.logpdf":"\nbase.dists.laplace.logpdf( x, μ, b )\n Evaluates the logarithm of the probability density function (PDF) for a\n Laplace distribution with scale parameter `b` and location parameter `μ` at\n a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.laplace.logpdf( 2.0, 0.0, 1.0 )\n ~-2.693\n > y = base.dists.laplace.logpdf( -1.0, 2.0, 3.0 )\n ~-2.792\n > y = base.dists.laplace.logpdf( 2.5, 2.0, 3.0 )\n ~-1.958\n > y = base.dists.laplace.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.laplace.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.laplace.logpdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.laplace.logpdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.laplace.logpdf.factory( μ, b )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Laplace distribution with scale parameter `b` and\n location parameter `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.laplace.logpdf.factory( 10.0, 2.0 );\n > var y = mylogPDF( 10.0 )\n ~-1.386\n\n","base.dists.laplace.logpdf.factory":"\nbase.dists.laplace.logpdf.factory( μ, b )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Laplace distribution with scale parameter `b` and\n location parameter `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.laplace.logpdf.factory( 10.0, 2.0 );\n > var y = mylogPDF( 10.0 )\n ~-1.386","base.dists.laplace.mean":"\nbase.dists.laplace.mean( μ, b )\n Returns the expected value of a Laplace distribution with location parameter\n `μ` and scale parameter `b`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var y = base.dists.laplace.mean( 0.0, 1.0 )\n 0.0\n > y = base.dists.laplace.mean( 4.0, 2.0 )\n 4.0\n > y = base.dists.laplace.mean( NaN, 1.0 )\n NaN\n > y = base.dists.laplace.mean( 0.0, NaN )\n NaN\n > y = base.dists.laplace.mean( 0.0, 0.0 )\n NaN\n\n","base.dists.laplace.median":"\nbase.dists.laplace.median( μ, b )\n Returns the median of a Laplace distribution with location parameter `μ` and\n scale parameter `b`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var y = base.dists.laplace.median( 0.0, 1.0 )\n 0.0\n > y = base.dists.laplace.median( 4.0, 2.0 )\n 4.0\n > y = base.dists.laplace.median( NaN, 1.0 )\n NaN\n > y = base.dists.laplace.median( 0.0, NaN )\n NaN\n > y = base.dists.laplace.median( 0.0, 0.0 )\n NaN\n\n","base.dists.laplace.mgf":"\nbase.dists.laplace.mgf( t, μ, b )\n Evaluates the moment-generating function (MGF) for a Laplace\n distribution with scale parameter `b` and location parameter `μ` at a\n value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.laplace.mgf( 0.5, 0.0, 1.0 )\n ~1.333\n > y = base.dists.laplace.mgf( 0.0, 0.0, 1.0 )\n 1.0\n > y = base.dists.laplace.mgf( -1.0, 4.0, 0.2 )\n ~0.019\n > y = base.dists.laplace.mgf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.laplace.mgf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.laplace.mgf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.laplace.mgf( 1.0, 0.0, 2.0 )\n NaN\n > y = base.dists.laplace.mgf( -0.5, 0.0, 4.0 )\n NaN\n > y = base.dists.laplace.mgf( 2.0, 0.0, 0.0 )\n NaN\n > y = base.dists.laplace.mgf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.laplace.mgf.factory( μ, b )\n Returns a function for evaluating the moment-generating function (MGF)\n of a Laplace distribution with scale parameter `b` and location parameter\n `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.laplace.mgf.factory( 4.0, 2.0 );\n > var y = mymgf( 0.2 )\n ~2.649\n > y = mymgf( 0.4 )\n ~13.758\n\n","base.dists.laplace.mgf.factory":"\nbase.dists.laplace.mgf.factory( μ, b )\n Returns a function for evaluating the moment-generating function (MGF)\n of a Laplace distribution with scale parameter `b` and location parameter\n `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.laplace.mgf.factory( 4.0, 2.0 );\n > var y = mymgf( 0.2 )\n ~2.649\n > y = mymgf( 0.4 )\n ~13.758","base.dists.laplace.mode":"\nbase.dists.laplace.mode( μ, b )\n Returns the mode of a Laplace distribution with location parameter `μ` and\n scale parameter `b`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var y = base.dists.laplace.mode( 0.0, 1.0 )\n 0.0\n > y = base.dists.laplace.mode( 4.0, 2.0 )\n 4.0\n > y = base.dists.laplace.mode( NaN, 1.0 )\n NaN\n > y = base.dists.laplace.mode( 0.0, NaN )\n NaN\n > y = base.dists.laplace.mode( 0.0, 0.0 )\n NaN\n\n","base.dists.laplace.pdf":"\nbase.dists.laplace.pdf( x, μ, b )\n Evaluates the probability density function (PDF) for a Laplace\n distribution with scale parameter `b` and location parameter `μ` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.laplace.pdf( 2.0, 0.0, 1.0 )\n ~0.068\n > y = base.dists.laplace.pdf( -1.0, 2.0, 3.0 )\n ~0.061\n > y = base.dists.laplace.pdf( 2.5, 2.0, 3.0 )\n ~0.141\n > y = base.dists.laplace.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.laplace.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.laplace.pdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.laplace.pdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.laplace.pdf.factory( μ, b )\n Returns a function for evaluating the probability density function (PDF)\n of a Laplace distribution with scale parameter `b` and location parameter\n `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.laplace.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n 0.25\n\n","base.dists.laplace.pdf.factory":"\nbase.dists.laplace.pdf.factory( μ, b )\n Returns a function for evaluating the probability density function (PDF)\n of a Laplace distribution with scale parameter `b` and location parameter\n `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.laplace.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n 0.25","base.dists.laplace.quantile":"\nbase.dists.laplace.quantile( p, μ, b )\n Evaluates the quantile function for a Laplace distribution with scale\n parameter `b` and location parameter `μ` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.laplace.quantile( 0.8, 0.0, 1.0 )\n ~0.916\n > y = base.dists.laplace.quantile( 0.5, 4.0, 2.0 )\n 4.0\n\n > y = base.dists.laplace.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.laplace.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.laplace.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.laplace.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.laplace.quantile( 0.0, 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.laplace.quantile( 0.5, 0.0, -1.0 )\n NaN\n\n\nbase.dists.laplace.quantile.factory( μ, b )\n Returns a function for evaluating the quantile function of a Laplace\n distribution with scale parameter `b` and location parameter `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.laplace.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0\n > y = myQuantile( 0.8 )\n ~11.833\n\n","base.dists.laplace.quantile.factory":"\nbase.dists.laplace.quantile.factory( μ, b )\n Returns a function for evaluating the quantile function of a Laplace\n distribution with scale parameter `b` and location parameter `μ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.laplace.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0\n > y = myQuantile( 0.8 )\n ~11.833","base.dists.laplace.skewness":"\nbase.dists.laplace.skewness( μ, b )\n Returns the skewness of a Laplace distribution with location parameter `μ`\n and scale parameter `b`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var y = base.dists.laplace.skewness( 0.0, 1.0 )\n 0.0\n > y = base.dists.laplace.skewness( 4.0, 2.0 )\n 0.0\n > y = base.dists.laplace.skewness( NaN, 1.0 )\n NaN\n > y = base.dists.laplace.skewness( 0.0, NaN )\n NaN\n > y = base.dists.laplace.skewness( 0.0, 0.0 )\n NaN\n\n","base.dists.laplace.stdev":"\nbase.dists.laplace.stdev( μ, b )\n Returns the standard deviation of a Laplace distribution with location\n parameter `μ` and scale parameter `b`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var y = base.dists.laplace.stdev( 0.0, 1.0 )\n ~1.414\n > y = base.dists.laplace.stdev( 4.0, 2.0 )\n ~2.828\n > y = base.dists.laplace.stdev( NaN, 1.0 )\n NaN\n > y = base.dists.laplace.stdev( 0.0, NaN )\n NaN\n > y = base.dists.laplace.stdev( 0.0, 0.0 )\n NaN\n\n","base.dists.laplace.variance":"\nbase.dists.laplace.variance( μ, b )\n Returns the variance of a Laplace distribution with location parameter `μ`\n and scale parameter `b`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `b <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n b: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var y = base.dists.laplace.variance( 0.0, 1.0 )\n 2.0\n > y = base.dists.laplace.variance( 4.0, 2.0 )\n 8.0\n > y = base.dists.laplace.variance( NaN, 1.0 )\n NaN\n > y = base.dists.laplace.variance( 0.0, NaN )\n NaN\n > y = base.dists.laplace.variance( 0.0, 0.0 )\n NaN\n\n","base.dists.levy.cdf":"\nbase.dists.levy.cdf( x, μ, c )\n Evaluates the cumulative distribution function (CDF) for a Lévy distribution\n with location parameter `μ` and scale parameter `c` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.levy.cdf( 2.0, 0.0, 1.0 )\n ~0.48\n > y = base.dists.levy.cdf( 12.0, 10.0, 3.0 )\n ~0.221\n > y = base.dists.levy.cdf( 9.0, 10.0, 3.0 )\n 0.0\n > y = base.dists.levy.cdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.levy.cdf( 2, NaN, 1.0 )\n NaN\n > y = base.dists.levy.cdf( 2.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.levy.cdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.levy.cdf.factory( μ, c )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Lévy distribution with location parameter `μ` and scale parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.levy.cdf.factory( 2.0, 3.0 );\n > var y = myCDF( 10.0 )\n ~0.54\n > y = myCDF( 2.0 )\n 0.0\n\n","base.dists.levy.cdf.factory":"\nbase.dists.levy.cdf.factory( μ, c )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Lévy distribution with location parameter `μ` and scale parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.levy.cdf.factory( 2.0, 3.0 );\n > var y = myCDF( 10.0 )\n ~0.54\n > y = myCDF( 2.0 )\n 0.0","base.dists.levy.entropy":"\nbase.dists.levy.entropy( μ, c )\n Returns the entropy of a Lévy distribution with location parameter `μ` and\n scale parameter `c`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var y = base.dists.levy.entropy( 0.0, 1.0 )\n ~3.324\n > y = base.dists.levy.entropy( 4.0, 2.0 )\n ~4.018\n > y = base.dists.levy.entropy( NaN, 1.0 )\n NaN\n > y = base.dists.levy.entropy( 0.0, NaN )\n NaN\n > y = base.dists.levy.entropy( 0.0, 0.0 )\n NaN\n\n","base.dists.levy.Levy":"\nbase.dists.levy.Levy( [μ, c] )\n Returns a Lévy distribution object.\n\n Parameters\n ----------\n μ: number (optional)\n Location parameter. Default: `0.0`.\n\n c: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n levy: Object\n Distribution instance.\n\n levy.mu: number\n Location parameter.\n\n levy.c: number\n Scale parameter. If set, the value must be greater than `0`.\n\n levy.entropy: number\n Read-only property which returns the differential entropy.\n\n levy.mean: number\n Read-only property which returns the expected value.\n\n levy.median: number\n Read-only property which returns the median.\n\n levy.mode: number\n Read-only property which returns the mode.\n\n levy.stdev: number\n Read-only property which returns the standard deviation.\n\n levy.variance: number\n Read-only property which returns the variance.\n\n levy.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n levy.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n levy.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n levy.pdf: Function\n Evaluates the probability density function (PDF).\n\n levy.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var levy = base.dists.levy.Levy( -2.0, 3.0 );\n > levy.mu\n -2.0\n > levy.c\n 3.0\n > levy.entropy\n ~4.423\n > levy.mean\n Infinity\n > levy.median\n ~4.594\n > levy.mode\n -1.0\n > levy.stdev\n Infinity\n > levy.variance\n Infinity\n > levy.cdf( 0.8 )\n ~0.3\n > levy.logcdf( 0.8 )\n ~-1.2\n > levy.logpdf( 1.0 )\n ~-2.518\n > levy.pdf( 1.0 )\n ~0.081\n > levy.quantile( 0.8 )\n ~44.74\n\n","base.dists.levy.logcdf":"\nbase.dists.levy.logcdf( x, μ, c )\n Evaluates the logarithm of the cumulative distribution function (CDF) for a\n Lévy distribution with location parameter `μ` and scale parameter `c` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.levy.logcdf( 2.0, 0.0, 1.0 )\n ~-0.735\n > y = base.dists.levy.logcdf( 12.0, 10.0, 3.0 )\n ~-1.51\n > y = base.dists.levy.logcdf( 9.0, 10.0, 3.0 )\n -Infinity\n > y = base.dists.levy.logcdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.levy.logcdf( 2, NaN, 1.0 )\n NaN\n > y = base.dists.levy.logcdf( 2.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.levy.logcdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.levy.logcdf.factory( μ, c )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Lévy distribution with location parameter\n `μ` and scale parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.levy.logcdf.factory( 2.0, 3.0 );\n > var y = mylogcdf( 10.0 )\n ~-0.616\n > y = mylogcdf( 2.0 )\n -Infinity\n\n","base.dists.levy.logcdf.factory":"\nbase.dists.levy.logcdf.factory( μ, c )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Lévy distribution with location parameter\n `μ` and scale parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.levy.logcdf.factory( 2.0, 3.0 );\n > var y = mylogcdf( 10.0 )\n ~-0.616\n > y = mylogcdf( 2.0 )\n -Infinity","base.dists.levy.logpdf":"\nbase.dists.levy.logpdf( x, μ, c )\n Evaluates the logarithm of the probability density function (PDF) for a Lévy\n distribution with location parameter `μ` and scale parameter `c` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.levy.logpdf( 2.0, 0.0, 1.0 )\n ~-2.209\n > y = base.dists.levy.logpdf( -1.0, 4.0, 2.0 )\n -Infinity\n > y = base.dists.levy.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.levy.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.levy.logpdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.levy.logpdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.levy.logpdf.factory( μ, c )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Lévy distribution with location parameter `μ` and scale\n parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.levy.logpdf.factory( 10.0, 2.0 );\n > var y = mylogPDF( 11.0 )\n ~-1.572\n\n","base.dists.levy.logpdf.factory":"\nbase.dists.levy.logpdf.factory( μ, c )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Lévy distribution with location parameter `μ` and scale\n parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.levy.logpdf.factory( 10.0, 2.0 );\n > var y = mylogPDF( 11.0 )\n ~-1.572","base.dists.levy.mean":"\nbase.dists.levy.mean( μ, c )\n Returns the expected value of a Lévy distribution with location parameter\n `μ` and scale parameter `c`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var y = base.dists.levy.mean( 0.0, 1.0 )\n Infinity\n > y = base.dists.levy.mean( 4.0, 3.0 )\n Infinity\n > y = base.dists.levy.mean( NaN, 1.0 )\n NaN\n > y = base.dists.levy.mean( 0.0, NaN )\n NaN\n > y = base.dists.levy.mean( 0.0, 0.0 )\n NaN\n\n","base.dists.levy.median":"\nbase.dists.levy.median( μ, c )\n Returns the median of a Lévy distribution with location parameter `μ` and\n scale parameter `c`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var y = base.dists.levy.median( 0.0, 1.0 )\n ~2.198\n > y = base.dists.levy.median( 4.0, 3.0 )\n ~10.594\n > y = base.dists.levy.median( NaN, 1.0 )\n NaN\n > y = base.dists.levy.median( 0.0, NaN )\n NaN\n > y = base.dists.levy.median( 0.0, 0.0 )\n NaN\n\n","base.dists.levy.mode":"\nbase.dists.levy.mode( μ, c )\n Returns the mode of a Lévy distribution with location parameter `μ` and\n scale parameter `c`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var y = base.dists.levy.mode( 0.0, 1.0 )\n ~0.333\n > y = base.dists.levy.mode( 4.0, 3.0 )\n 5.0\n > y = base.dists.levy.mode( NaN, 1.0 )\n NaN\n > y = base.dists.levy.mode( 0.0, NaN )\n NaN\n > y = base.dists.levy.mode( 0.0, 0.0 )\n NaN\n\n","base.dists.levy.pdf":"\nbase.dists.levy.pdf( x, μ, c )\n Evaluates the probability density function (PDF) for a Lévy distribution\n with location parameter `μ` and scale parameter `c` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.levy.pdf( 2.0, 0.0, 1.0 )\n ~0.11\n > y = base.dists.levy.pdf( -1.0, 4.0, 2.0 )\n 0.0\n > y = base.dists.levy.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.levy.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.levy.pdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.levy.pdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.levy.pdf.factory( μ, c )\n Returns a function for evaluating the probability density function (PDF) of\n a Lévy distribution with location parameter `μ` and scale parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.levy.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 11.0 )\n ~0.208\n\n","base.dists.levy.pdf.factory":"\nbase.dists.levy.pdf.factory( μ, c )\n Returns a function for evaluating the probability density function (PDF) of\n a Lévy distribution with location parameter `μ` and scale parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.levy.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 11.0 )\n ~0.208","base.dists.levy.quantile":"\nbase.dists.levy.quantile( p, μ, c )\n Evaluates the quantile function for a Lévy distribution with location\n parameter `μ` and scale parameter `c` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.levy.quantile( 0.8, 0.0, 1.0 )\n ~15.58\n > y = base.dists.levy.quantile( 0.5, 4.0, 2.0 )\n ~8.396\n\n > y = base.dists.levy.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.levy.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.levy.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.levy.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.levy.quantile( 0.0, 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.levy.quantile( 0.5, 0.0, -1.0 )\n NaN\n\n\nbase.dists.levy.quantile.factory( μ, c )\n Returns a function for evaluating the quantile function of a Lévy\n distribution with location parameter `μ` and scale parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.levy.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n ~14.396\n\n","base.dists.levy.quantile.factory":"\nbase.dists.levy.quantile.factory( μ, c )\n Returns a function for evaluating the quantile function of a Lévy\n distribution with location parameter `μ` and scale parameter `c`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.levy.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n ~14.396","base.dists.levy.stdev":"\nbase.dists.levy.stdev( μ, c )\n Returns the standard deviation of a Lévy distribution with location\n parameter `μ` and scale parameter `c`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var y = base.dists.levy.stdev( 0.0, 1.0 )\n Infinity\n > y = base.dists.levy.stdev( 4.0, 3.0 )\n Infinity\n > y = base.dists.levy.stdev( NaN, 1.0 )\n NaN\n > y = base.dists.levy.stdev( 0.0, NaN )\n NaN\n > y = base.dists.levy.stdev( 0.0, 0.0 )\n NaN\n\n","base.dists.levy.variance":"\nbase.dists.levy.variance( μ, c )\n Returns the variance of a Lévy distribution with location parameter `μ` and\n scale parameter `c`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `c <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n c: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var y = base.dists.levy.variance( 0.0, 1.0 )\n Infinity\n > y = base.dists.levy.variance( 4.0, 3.0 )\n Infinity\n > y = base.dists.levy.variance( NaN, 1.0 )\n NaN\n > y = base.dists.levy.variance( 0.0, NaN )\n NaN\n > y = base.dists.levy.variance( 0.0, 0.0 )\n NaN\n\n","base.dists.logistic.cdf":"\nbase.dists.logistic.cdf( x, μ, s )\n Evaluates the cumulative distribution function (CDF) for a logistic\n distribution with location parameter `μ` and scale parameter `s` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.logistic.cdf( 2.0, 0.0, 1.0 )\n ~0.881\n > y = base.dists.logistic.cdf( 5.0, 10.0, 3.0 )\n ~0.159\n\n > y = base.dists.logistic.cdf( 2.0, 0.0, NaN )\n NaN\n > y = base.dists.logistic.cdf( 2.0, NaN, 1.0 )\n NaN\n > y = base.dists.logistic.cdf( NaN, 0.0, 1.0 )\n NaN\n\n // Degenerate distribution centered at `μ` when `s = 0.0`:\n > y = base.dists.logistic.cdf( 2.0, 8.0, 0.0 )\n 0.0\n > y = base.dists.logistic.cdf( 8.0, 8.0, 0.0 )\n 1.0\n > y = base.dists.logistic.cdf( 10.0, 8.0, 0.0 )\n 1.0\n\n\nbase.dists.logistic.cdf.factory( μ, s )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a logistic distribution with location parameter `μ` and scale parameter\n `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.logistic.cdf.factory( 3.0, 1.5 );\n > var y = mycdf( 1.0 )\n ~0.209\n\n","base.dists.logistic.cdf.factory":"\nbase.dists.logistic.cdf.factory( μ, s )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a logistic distribution with location parameter `μ` and scale parameter\n `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.logistic.cdf.factory( 3.0, 1.5 );\n > var y = mycdf( 1.0 )\n ~0.209","base.dists.logistic.entropy":"\nbase.dists.logistic.entropy( μ, s )\n Returns the entropy of a logistic distribution with location parameter `μ`\n and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var y = base.dists.logistic.entropy( 0.0, 1.0 )\n 2.0\n > y = base.dists.logistic.entropy( 4.0, 2.0 )\n ~2.693\n > y = base.dists.logistic.entropy( NaN, 1.0 )\n NaN\n > y = base.dists.logistic.entropy( 0.0, NaN )\n NaN\n > y = base.dists.logistic.entropy( 0.0, 0.0 )\n NaN\n\n","base.dists.logistic.kurtosis":"\nbase.dists.logistic.kurtosis( μ, s )\n Returns the excess kurtosis of a logistic distribution with location\n parameter `μ` and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var y = base.dists.logistic.kurtosis( 0.0, 1.0 )\n 1.2\n > y = base.dists.logistic.kurtosis( 4.0, 2.0 )\n 1.2\n > y = base.dists.logistic.kurtosis( NaN, 1.0 )\n NaN\n > y = base.dists.logistic.kurtosis( 0.0, NaN )\n NaN\n > y = base.dists.logistic.kurtosis( 0.0, 0.0 )\n NaN\n\n\n","base.dists.logistic.logcdf":"\nbase.dists.logistic.logcdf( x, μ, s )\n Evaluates the logarithm of the cumulative distribution function (CDF) for a\n logistic distribution with location parameter `μ` and scale parameter `s` at\n a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.logistic.logcdf( 2.0, 0.0, 1.0 )\n ~-0.127\n > y = base.dists.logistic.logcdf( 5.0, 10.0, 3.0 )\n ~-1.84\n > y = base.dists.logistic.logcdf( 2.0, 0.0, NaN )\n NaN\n > y = base.dists.logistic.logcdf( 2, NaN, 1.0 )\n NaN\n > y = base.dists.logistic.logcdf( NaN, 0.0, 1.0 )\n NaN\n\n\nbase.dists.logistic.logcdf.factory( μ, s )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Logistic distribution with location\n parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.logistic.logcdf.factory( 3.0, 1.5 );\n > var y = mylogcdf( 1.0 )\n ~-1.567\n\n","base.dists.logistic.logcdf.factory":"\nbase.dists.logistic.logcdf.factory( μ, s )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Logistic distribution with location\n parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.logistic.logcdf.factory( 3.0, 1.5 );\n > var y = mylogcdf( 1.0 )\n ~-1.567","base.dists.logistic.Logistic":"\nbase.dists.logistic.Logistic( [μ, s] )\n Returns a logistic distribution object.\n\n Parameters\n ----------\n μ: number (optional)\n Location parameter. Default: `0.0`.\n\n s: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n logistic: Object\n Distribution instance.\n\n logistic.mu: number\n Location parameter.\n\n logistic.s: number\n Scale parameter. If set, the value must be greater than `0`.\n\n logistic.entropy: number\n Read-only property which returns the differential entropy.\n\n logistic.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n logistic.mean: number\n Read-only property which returns the expected value.\n\n logistic.median: number\n Read-only property which returns the median.\n\n logistic.mode: number\n Read-only property which returns the mode.\n\n logistic.skewness: number\n Read-only property which returns the skewness.\n\n logistic.stdev: number\n Read-only property which returns the standard deviation.\n\n logistic.variance: number\n Read-only property which returns the variance.\n\n logistic.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n logistic.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n logistic.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n logistic.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n logistic.pdf: Function\n Evaluates the probability density function (PDF).\n\n logistic.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var logistic = base.dists.logistic.Logistic( -2.0, 3.0 );\n > logistic.mu\n -2.0\n > logistic.s\n 3.0\n > logistic.entropy\n ~3.1\n > logistic.kurtosis\n 1.2\n > logistic.mean\n -2.0\n > logistic.median\n -2.0\n > logistic.mode\n -2.0\n > logistic.skewness\n 0.0\n > logistic.stdev\n ~5.441\n > logistic.variance\n ~29.609\n > logistic.cdf( 0.8 )\n ~0.718\n > logistic.logcdf( 0.8 )\n ~-0.332\n > logistic.logpdf( 2.0 )\n ~-2.9\n > logistic.mgf( 0.2 )\n ~1.329\n > logistic.pdf( 2.0 )\n ~0.055\n > logistic.quantile( 0.9 )\n ~4.592\n\n","base.dists.logistic.logpdf":"\nbase.dists.logistic.logpdf( x, μ, s )\n Evaluates the logarithm of the probability density function (PDF) for a\n logistic distribution with location parameter `μ` and scale parameter `s` at\n a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.logistic.logpdf( 2.0, 0.0, 1.0 )\n ~-2.254\n > y = base.dists.logistic.logpdf( -1.0, 4.0, 2.0 )\n ~-3.351\n > y = base.dists.logistic.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.logistic.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.logistic.logpdf( 0.0, 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.logistic.logpdf( 2.0, 0.0, -1.0 )\n NaN\n\n // Degenerate distribution at `s = 0.0`:\n > y = base.dists.logistic.logpdf( 2.0, 8.0, 0.0 )\n -Infinity\n > y = base.dists.logistic.logpdf( 8.0, 8.0, 0.0 )\n Infinity\n\n\nbase.dists.logistic.logpdf.factory( μ, s )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Logistic distribution with location parameter `μ` and\n scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.logistic.logpdf.factory( 10.0, 2.0 );\n > var y = mylogpdf( 10.0 )\n ~-2.079\n\n","base.dists.logistic.logpdf.factory":"\nbase.dists.logistic.logpdf.factory( μ, s )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Logistic distribution with location parameter `μ` and\n scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.logistic.logpdf.factory( 10.0, 2.0 );\n > var y = mylogpdf( 10.0 )\n ~-2.079","base.dists.logistic.mean":"\nbase.dists.logistic.mean( μ, s )\n Returns the expected value of a logistic distribution with location\n parameter `μ` and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var y = base.dists.logistic.mean( 0.0, 1.0 )\n 0.0\n > y = base.dists.logistic.mean( 4.0, 2.0 )\n 4.0\n > y = base.dists.logistic.mean( NaN, 1.0 )\n NaN\n > y = base.dists.logistic.mean( 0.0, NaN )\n NaN\n > y = base.dists.logistic.mean( 0.0, 0.0 )\n NaN\n\n","base.dists.logistic.median":"\nbase.dists.logistic.median( μ, s )\n Returns the median of a logistic distribution with location parameter `μ`\n and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var y = base.dists.logistic.median( 0.0, 1.0 )\n 0.0\n > y = base.dists.logistic.median( 4.0, 2.0 )\n 4.0\n > y = base.dists.logistic.median( NaN, 1.0 )\n NaN\n > y = base.dists.logistic.median( 0.0, NaN )\n NaN\n > y = base.dists.logistic.median( 0.0, 0.0 )\n NaN\n\n","base.dists.logistic.mgf":"\nbase.dists.logistic.mgf( t, μ, s )\n Evaluates the moment-generating function (MGF) for a logistic distribution\n with location parameter `μ` and scale parameter `s` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.logistic.mgf( 0.9, 0.0, 1.0 )\n ~9.15\n > y = base.dists.logistic.mgf( 0.1, 4.0, 4.0 )\n ~1.971\n > y = base.dists.logistic.mgf( -0.2, 4.0, 4.0 )\n ~1.921\n > y = base.dists.logistic.mgf( 0.5, 0.0, -1.0 )\n NaN\n > y = base.dists.logistic.mgf( 0.5, 0.0, 4.0 )\n Infinity\n > y = base.dists.logistic.mgf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.logistic.mgf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.logistic.mgf( 0.0, 0.0, NaN )\n NaN\n\n\nbase.dists.logistic.mgf.factory( μ, s )\n Returns a function for evaluating the moment-generating function (MGF)\n of a Logistic distribution with location parameter `μ` and scale parameter\n `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.logistic.mgf.factory( 10.0, 0.5 );\n > var y = mymgf( 0.5 )\n ~164.846\n > y = mymgf( 2.0 )\n Infinity\n\n","base.dists.logistic.mgf.factory":"\nbase.dists.logistic.mgf.factory( μ, s )\n Returns a function for evaluating the moment-generating function (MGF)\n of a Logistic distribution with location parameter `μ` and scale parameter\n `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.logistic.mgf.factory( 10.0, 0.5 );\n > var y = mymgf( 0.5 )\n ~164.846\n > y = mymgf( 2.0 )\n Infinity","base.dists.logistic.mode":"\nbase.dists.logistic.mode( μ, s )\n Returns the mode of a logistic distribution with location parameter `μ` and\n scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var y = base.dists.logistic.mode( 0.0, 1.0 )\n 0.0\n > y = base.dists.logistic.mode( 4.0, 2.0 )\n 4.0\n > y = base.dists.logistic.mode( NaN, 1.0 )\n NaN\n > y = base.dists.logistic.mode( 0.0, NaN )\n NaN\n > y = base.dists.logistic.mode( 0.0, 0.0 )\n NaN\n\n","base.dists.logistic.pdf":"\nbase.dists.logistic.pdf( x, μ, s )\n Evaluates the probability density function (PDF) for a logistic distribution\n with location parameter `μ` and scale parameter `s` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.logistic.pdf( 2.0, 0.0, 1.0 )\n ~0.105\n > y = base.dists.logistic.pdf( -1.0, 4.0, 2.0 )\n ~0.035\n > y = base.dists.logistic.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.logistic.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.logistic.pdf( 0.0, 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.logistic.pdf( 2.0, 0.0, -1.0 )\n NaN\n > y = base.dists.logistic.pdf( 2.0, 8.0, 0.0 )\n 0.0\n > y = base.dists.logistic.pdf( 8.0, 8.0, 0.0 )\n Infinity\n\n\nbase.dists.logistic.pdf.factory( μ, s )\n Returns a function for evaluating the probability density function (PDF) of\n a Logistic distribution with location parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.logistic.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n 0.125\n\n","base.dists.logistic.pdf.factory":"\nbase.dists.logistic.pdf.factory( μ, s )\n Returns a function for evaluating the probability density function (PDF) of\n a Logistic distribution with location parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.logistic.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n 0.125","base.dists.logistic.quantile":"\nbase.dists.logistic.quantile( p, μ, s )\n Evaluates the quantile function for a logistic distribution with location\n parameter `μ` and scale parameter `s` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.logistic.quantile( 0.8, 0.0, 1.0 )\n ~1.386\n > y = base.dists.logistic.quantile( 0.5, 4.0, 2.0 )\n 4\n\n > y = base.dists.logistic.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.logistic.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.logistic.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.logistic.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.logistic.quantile( 0.0, 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.logistic.quantile( 0.5, 0.0, -1.0 )\n NaN\n\n\nbase.dists.logistic.quantile.factory( μ, s )\n Returns a function for evaluating the quantile function of a logistic\n distribution with location parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.logistic.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0\n\n","base.dists.logistic.quantile.factory":"\nbase.dists.logistic.quantile.factory( μ, s )\n Returns a function for evaluating the quantile function of a logistic\n distribution with location parameter `μ` and scale parameter `s`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.logistic.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0","base.dists.logistic.skewness":"\nbase.dists.logistic.skewness( μ, s )\n Returns the skewness of a logistic distribution with location parameter `μ`\n and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var y = base.dists.logistic.skewness( 0.0, 1.0 )\n 0.0\n > y = base.dists.logistic.skewness( 4.0, 2.0 )\n 0.0\n > y = base.dists.logistic.skewness( NaN, 1.0 )\n NaN\n > y = base.dists.logistic.skewness( 0.0, NaN )\n NaN\n > y = base.dists.logistic.skewness( 0.0, 0.0 )\n NaN\n\n","base.dists.logistic.stdev":"\nbase.dists.logistic.stdev( μ, s )\n Returns the standard deviation of a logistic distribution with location\n parameter `μ` and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var y = base.dists.logistic.stdev( 0.0, 1.0 )\n ~1.814\n > y = base.dists.logistic.stdev( 4.0, 2.0 )\n ~3.628\n > y = base.dists.logistic.stdev( NaN, 1.0 )\n NaN\n > y = base.dists.logistic.stdev( 0.0, NaN )\n NaN\n > y = base.dists.logistic.stdev( 0.0, 0.0 )\n NaN\n\n","base.dists.logistic.variance":"\nbase.dists.logistic.variance( μ, s )\n Returns the variance of a logistic distribution with location parameter `μ`\n and scale parameter `s`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `s <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n s: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var y = base.dists.logistic.variance( 0.0, 1.0 )\n ~3.29\n > y = base.dists.logistic.variance( 4.0, 2.0 )\n ~13.159\n > y = base.dists.logistic.variance( NaN, 1.0 )\n NaN\n > y = base.dists.logistic.variance( 0.0, NaN )\n NaN\n > y = base.dists.logistic.variance( 0.0, 0.0 )\n NaN\n\n","base.dists.lognormal.cdf":"\nbase.dists.lognormal.cdf( x, μ, σ )\n Evaluates the cumulative distribution function (CDF) for a lognormal\n distribution with location parameter `μ` and scale parameter `σ` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.lognormal.cdf( 2.0, 0.0, 1.0 )\n ~0.756\n > y = base.dists.lognormal.cdf( 5.0, 10.0, 3.0 )\n ~0.003\n\n > y = base.dists.lognormal.cdf( 2.0, 0.0, NaN )\n NaN\n > y = base.dists.lognormal.cdf( 2.0, NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.cdf( NaN, 0.0, 1.0 )\n NaN\n\n // Non-positive scale parameter `σ`:\n > y = base.dists.lognormal.cdf( 2.0, 0.0, -1.0 )\n NaN\n > y = base.dists.lognormal.cdf( 2.0, 0.0, 0.0 )\n NaN\n\n\nbase.dists.lognormal.cdf.factory( μ, σ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a lognormal distribution with location parameter `μ` and scale parameter\n `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.lognormal.cdf.factory( 3.0, 1.5 );\n > var y = myCDF( 1.0 )\n ~0.023\n > y = myCDF( 4.0 )\n ~0.141\n\n","base.dists.lognormal.cdf.factory":"\nbase.dists.lognormal.cdf.factory( μ, σ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a lognormal distribution with location parameter `μ` and scale parameter\n `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.lognormal.cdf.factory( 3.0, 1.5 );\n > var y = myCDF( 1.0 )\n ~0.023\n > y = myCDF( 4.0 )\n ~0.141","base.dists.lognormal.entropy":"\nbase.dists.lognormal.entropy( μ, σ )\n Returns the differential entropy of a lognormal distribution with location\n `μ` and scale `σ` (in nats).\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var y = base.dists.lognormal.entropy( 0.0, 1.0 )\n ~1.419\n > y = base.dists.lognormal.entropy( 5.0, 2.0 )\n ~7.112\n > y = base.dists.lognormal.entropy( NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.entropy( 0.0, NaN )\n NaN\n > y = base.dists.lognormal.entropy( 0.0, 0.0 )\n NaN\n\n","base.dists.lognormal.kurtosis":"\nbase.dists.lognormal.kurtosis( μ, σ )\n Returns the excess kurtosis of a lognormal distribution with location `μ`\n and scale `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Kurtosis.\n\n Examples\n --------\n > var y = base.dists.lognormal.kurtosis( 0.0, 1.0 )\n ~110.936\n > y = base.dists.lognormal.kurtosis( 5.0, 2.0 )\n ~9220556.977\n > y = base.dists.lognormal.kurtosis( NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.kurtosis( 0.0, NaN )\n NaN\n > y = base.dists.lognormal.kurtosis( 0.0, 0.0 )\n NaN\n\n","base.dists.lognormal.LogNormal":"\nbase.dists.lognormal.LogNormal( [μ, σ] )\n Returns a lognormal distribution object.\n\n Parameters\n ----------\n μ: number (optional)\n Location parameter. Default: `0.0`.\n\n σ: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n lognormal: Object\n Distribution instance.\n\n lognormal.mu: number\n Location parameter.\n\n lognormal.sigma: number\n Scale parameter. If set, the value must be greater than `0`.\n\n lognormal.entropy: number\n Read-only property which returns the differential entropy.\n\n lognormal.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n lognormal.mean: number\n Read-only property which returns the expected value.\n\n lognormal.median: number\n Read-only property which returns the median.\n\n lognormal.mode: number\n Read-only property which returns the mode.\n\n lognormal.skewness: number\n Read-only property which returns the skewness.\n\n lognormal.stdev: number\n Read-only property which returns the standard deviation.\n\n lognormal.variance: number\n Read-only property which returns the variance.\n\n lognormal.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n lognormal.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n lognormal.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n lognormal.pdf: Function\n Evaluates the probability density function (PDF).\n\n lognormal.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var lognormal = base.dists.lognormal.LogNormal( -2.0, 3.0 );\n > lognormal.mu\n -2.0\n > lognormal.sigma\n 3.0\n > lognormal.entropy\n ~0.518\n > lognormal.kurtosis\n 4312295840576300\n > lognormal.mean\n ~12.182\n > lognormal.median\n ~0.135\n > lognormal.mode\n ~0.0\n > lognormal.skewness\n ~729551.383\n > lognormal.stdev\n ~1096.565\n > lognormal.variance\n ~1202455.871\n > lognormal.cdf( 0.8 )\n ~0.723\n > lognormal.logcdf( 0.8 )\n ~-4.334\n > lognormal.logpdf( 2.0 )\n ~-3.114\n > lognormal.pdf( 2.0 )\n ~0.044\n > lognormal.quantile( 0.9 )\n ~6.326\n\n","base.dists.lognormal.logcdf":"\nbase.dists.lognormal.logcdf( x, μ, σ )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a lognormal distribution with mean `μ` and standard deviation `σ`\n at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Evaluated logcdf.\n\n Examples\n --------\n > var y = base.dists.lognormal.logcdf( 2.0, 0.0, 1.0 )\n ~-0.2799\n > y = base.dists.lognormal.logcdf( 13.0, 4.0, 2.0 )\n ~-1.442\n > y = base.dists.lognormal.logcdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.lognormal.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.logcdf( 0.0, 0.0, NaN )\n NaN\n\n // Negative standard deviation:\n > y = base.dists.lognormal.logcdf( 2.0, 0.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `μ` when `σ = 0.0`:\n > y = base.dists.lognormal.logcdf( 2.0, 8.0, 0.0 )\n -Infinity\n > y = base.dists.lognormal.logcdf( 8.0, 8.0, 0.0 )\n -Infinity\n\n\nbase.dists.lognormal.logcdf.factory( μ, σ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a lognormal distribution with mean `μ` and\n standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.lognormal.logcdf.factory( 10.0, 2.0 );\n > var y = mylogcdf( 10.0 )\n ~-9.732\n\n","base.dists.lognormal.logcdf.factory":"\nbase.dists.lognormal.logcdf.factory( μ, σ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a lognormal distribution with mean `μ` and\n standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.lognormal.logcdf.factory( 10.0, 2.0 );\n > var y = mylogcdf( 10.0 )\n ~-9.732","base.dists.lognormal.logpdf":"\nbase.dists.lognormal.logpdf( x, μ, σ )\n Evaluates the natural logarithm of the probability density function (PDF)\n for a lognormal distribution with location parameter `μ` and scale parameter\n `σ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.lognormal.logpdf( 2.0, 0.0, 1.0 )\n ~-1.852\n > y = base.dists.lognormal.logpdf( 1.0, 0.0, 1.0 )\n ~-0.919\n > y = base.dists.lognormal.logpdf( 1.0, 3.0, 1.0 )\n ~-5.419\n > y = base.dists.lognormal.logpdf( -1.0, 4.0, 2.0 )\n -Infinity\n\n > y = base.dists.lognormal.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.lognormal.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.logpdf( 0.0, 0.0, NaN )\n NaN\n\n // Non-positive scale parameter `σ`:\n > y = base.dists.lognormal.logpdf( 2.0, 0.0, -1.0 )\n NaN\n > y = base.dists.lognormal.logpdf( 2.0, 0.0, 0.0 )\n NaN\n\n\nbase.dists.lognormal.logpdf.factory( μ, σ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a lognormal distribution with location parameter\n `μ` and scale parameter `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.lognormal.logpdf.factory( 4.0, 2.0 );\n > var y = mylogPDF( 10.0 )\n ~-4.275\n > y = mylogPDF( 2.0 )\n ~-3.672\n\n","base.dists.lognormal.logpdf.factory":"\nbase.dists.lognormal.logpdf.factory( μ, σ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a lognormal distribution with location parameter\n `μ` and scale parameter `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.lognormal.logpdf.factory( 4.0, 2.0 );\n > var y = mylogPDF( 10.0 )\n ~-4.275\n > y = mylogPDF( 2.0 )\n ~-3.672","base.dists.lognormal.mean":"\nbase.dists.lognormal.mean( μ, σ )\n Returns the expected value of a lognormal distribution with location `μ` and\n scale `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var y = base.dists.lognormal.mean( 0.0, 1.0 )\n ~1.649\n > y = base.dists.lognormal.mean( 4.0, 2.0 )\n ~403.429\n > y = base.dists.lognormal.mean( NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.mean( 0.0, NaN )\n NaN\n > y = base.dists.lognormal.mean( 0.0, 0.0 )\n NaN\n\n","base.dists.lognormal.median":"\nbase.dists.lognormal.median( μ, σ )\n Returns the median of a lognormal distribution with location `μ` and scale\n `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var y = base.dists.lognormal.median( 0.0, 1.0 )\n 1.0\n > y = base.dists.lognormal.median( 5.0, 2.0 )\n ~148.413\n > y = base.dists.lognormal.median( NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.median( 0.0, NaN )\n NaN\n > y = base.dists.lognormal.median( 0.0, 0.0 )\n NaN\n\n","base.dists.lognormal.mode":"\nbase.dists.lognormal.mode( μ, σ )\n Returns the mode of a lognormal distribution with location `μ` and scale\n `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var y = base.dists.lognormal.mode( 0.0, 1.0 )\n ~0.368\n > y = base.dists.lognormal.mode( 5.0, 2.0 )\n ~2.718\n > y = base.dists.lognormal.mode( NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.mode( 0.0, NaN )\n NaN\n > y = base.dists.lognormal.mode( 0.0, 0.0 )\n NaN\n\n","base.dists.lognormal.pdf":"\nbase.dists.lognormal.pdf( x, μ, σ )\n Evaluates the probability density function (PDF) for a lognormal\n distribution with location parameter `μ` and scale parameter `σ` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.lognormal.pdf( 2.0, 0.0, 1.0 )\n ~0.157\n > y = base.dists.lognormal.pdf( 1.0, 0.0, 1.0 )\n ~0.399\n > y = base.dists.lognormal.pdf( 1.0, 3.0, 1.0 )\n ~0.004\n > y = base.dists.lognormal.pdf( -1.0, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.lognormal.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.lognormal.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.pdf( 0.0, 0.0, NaN )\n NaN\n\n // Non-positive scale parameter `σ`:\n > y = base.dists.lognormal.pdf( 2.0, 0.0, -1.0 )\n NaN\n > y = base.dists.lognormal.pdf( 2.0, 0.0, 0.0 )\n NaN\n\n\nbase.dists.lognormal.pdf.factory( μ, σ )\n Returns a function for evaluating the probability density function (PDF) of\n a lognormal distribution with location parameter `μ` and scale parameter\n `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.lognormal.pdf.factory( 4.0, 2.0 );\n > var y = myPDF( 10.0 )\n ~0.014\n > y = myPDF( 2.0 )\n ~0.025\n\n","base.dists.lognormal.pdf.factory":"\nbase.dists.lognormal.pdf.factory( μ, σ )\n Returns a function for evaluating the probability density function (PDF) of\n a lognormal distribution with location parameter `μ` and scale parameter\n `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.lognormal.pdf.factory( 4.0, 2.0 );\n > var y = myPDF( 10.0 )\n ~0.014\n > y = myPDF( 2.0 )\n ~0.025","base.dists.lognormal.quantile":"\nbase.dists.lognormal.quantile( p, μ, σ )\n Evaluates the quantile function for a lognormal distribution with location\n parameter `μ` and scale parameter `σ` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.lognormal.quantile( 0.8, 0.0, 1.0 )\n ~2.32\n > y = base.dists.lognormal.quantile( 0.5, 4.0, 2.0 )\n ~54.598\n > y = base.dists.lognormal.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.lognormal.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.lognormal.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.lognormal.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.quantile( 0.0, 0.0, NaN )\n NaN\n\n // Non-positive scale parameter `σ`:\n > y = base.dists.lognormal.quantile( 0.5, 0.0, -1.0 )\n NaN\n > y = base.dists.lognormal.quantile( 0.5, 0.0, 0.0 )\n NaN\n\n\nbase.dists.lognormal.quantile.factory( μ, σ )\n Returns a function for evaluating the quantile function of a lognormal\n distribution with location parameter `μ` and scale parameter `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.lognormal.quantile.factory( 4.0, 2.0 );\n > var y = myQuantile( 0.2 )\n ~10.143\n > y = myQuantile( 0.8 )\n ~293.901\n\n","base.dists.lognormal.quantile.factory":"\nbase.dists.lognormal.quantile.factory( μ, σ )\n Returns a function for evaluating the quantile function of a lognormal\n distribution with location parameter `μ` and scale parameter `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.lognormal.quantile.factory( 4.0, 2.0 );\n > var y = myQuantile( 0.2 )\n ~10.143\n > y = myQuantile( 0.8 )\n ~293.901","base.dists.lognormal.skewness":"\nbase.dists.lognormal.skewness( μ, σ )\n Returns the skewness of a lognormal distribution with location `μ` and scale\n `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var y = base.dists.lognormal.skewness( 0.0, 1.0 )\n ~6.185\n > y = base.dists.lognormal.skewness( 5.0, 2.0 )\n ~414.359\n > y = base.dists.lognormal.skewness( NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.skewness( 0.0, NaN )\n NaN\n > y = base.dists.lognormal.skewness( 0.0, 0.0 )\n NaN\n\n","base.dists.lognormal.stdev":"\nbase.dists.lognormal.stdev( μ, σ )\n Returns the standard deviation of a lognormal distribution with location `μ`\n and scale `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var y = base.dists.lognormal.stdev( 0.0, 1.0 )\n ~2.161\n > y = base.dists.lognormal.stdev( 4.0, 2.0 )\n ~2953.533\n > y = base.dists.lognormal.stdev( NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.stdev( 0.0, NaN )\n NaN\n > y = base.dists.lognormal.stdev( 0.0, 0.0 )\n NaN\n\n","base.dists.lognormal.variance":"\nbase.dists.lognormal.variance( μ, σ )\n Returns the variance of a lognormal distribution with location `μ` and scale\n `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var y = base.dists.lognormal.variance( 0.0, 1.0 )\n ~4.671\n > y = base.dists.lognormal.variance( 4.0, 2.0 )\n ~8723355.729\n > y = base.dists.lognormal.variance( NaN, 1.0 )\n NaN\n > y = base.dists.lognormal.variance( 0.0, NaN )\n NaN\n > y = base.dists.lognormal.variance( 0.0, 0.0 )\n NaN\n\n","base.dists.negativeBinomial.cdf":"\nbase.dists.negativeBinomial.cdf( x, r, p )\n Evaluates the cumulative distribution function (CDF) for a negative binomial\n distribution with number of successes until experiment is stopped `r` and\n success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.negativeBinomial.cdf( 5.0, 20.0, 0.8 )\n ~0.617\n > y = base.dists.negativeBinomial.cdf( 21.0, 20.0, 0.5 )\n ~0.622\n > y = base.dists.negativeBinomial.cdf( 5.0, 10.0, 0.4 )\n ~0.034\n > y = base.dists.negativeBinomial.cdf( 0.0, 10.0, 0.9 )\n ~0.349\n > y = base.dists.negativeBinomial.cdf( 21.0, 15.5, 0.5 )\n ~0.859\n > y = base.dists.negativeBinomial.cdf( 5.0, 7.4, 0.4 )\n ~0.131\n\n > y = base.dists.negativeBinomial.cdf( 2.0, 0.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.cdf( 2.0, -2.0, 0.5 )\n NaN\n\n > y = base.dists.negativeBinomial.cdf( NaN, 20.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.cdf( 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.cdf( 0.0, 20.0, NaN )\n NaN\n\n > y = base.dists.negativeBinomial.cdf( 2.0, 20, -1.0 )\n NaN\n > y = base.dists.negativeBinomial.cdf( 2.0, 20, 1.5 )\n NaN\n\n\nbase.dists.negativeBinomial.cdf.factory( r, p )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a negative binomial distribution with number of successes until\n experiment is stopped `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.negativeBinomial.cdf.factory( 10, 0.5 );\n > var y = myCDF( 3.0 )\n ~0.046\n > y = myCDF( 11.0 )\n ~0.668\n\n","base.dists.negativeBinomial.cdf.factory":"\nbase.dists.negativeBinomial.cdf.factory( r, p )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a negative binomial distribution with number of successes until\n experiment is stopped `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.negativeBinomial.cdf.factory( 10, 0.5 );\n > var y = myCDF( 3.0 )\n ~0.046\n > y = myCDF( 11.0 )\n ~0.668","base.dists.negativeBinomial.kurtosis":"\nbase.dists.negativeBinomial.kurtosis( r, p )\n Returns the excess kurtosis of a negative binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n r: integer\n Number of failures until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Kurtosis.\n\n Examples\n --------\n > var v = base.dists.negativeBinomial.kurtosis( 100, 0.2 )\n ~0.061\n > v = base.dists.negativeBinomial.kurtosis( 20, 0.5 )\n ~0.325\n\n","base.dists.negativeBinomial.logpmf":"\nbase.dists.negativeBinomial.logpmf( x, r, p )\n Evaluates the natural logarithm of the probability mass function (PMF) for a\n negative binomial distribution with number of successes until experiment is\n stopped `r` and success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated logPMF.\n\n Examples\n --------\n > var y = base.dists.negativeBinomial.logpmf( 5.0, 20.0, 0.8 )\n ~-1.853\n > y = base.dists.negativeBinomial.logpmf( 21.0, 20.0, 0.5 )\n ~-2.818\n > y = base.dists.negativeBinomial.logpmf( 5.0, 10.0, 0.4 )\n ~-4.115\n > y = base.dists.negativeBinomial.logpmf( 0.0, 10.0, 0.9 )\n ~-1.054\n > y = base.dists.negativeBinomial.logpmf( 21.0, 15.5, 0.5 )\n ~-3.292\n > y = base.dists.negativeBinomial.logpmf( 5.0, 7.4, 0.4 )\n ~-2.976\n\n > y = base.dists.negativeBinomial.logpmf( 2.0, 0.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.logpmf( 2.0, -2.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.logpmf( 2.0, 20, -1.0 )\n NaN\n > y = base.dists.negativeBinomial.logpmf( 2.0, 20, 1.5 )\n NaN\n\n > y = base.dists.negativeBinomial.logpmf( NaN, 20.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.logpmf( 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.logpmf( 0.0, 20.0, NaN )\n NaN\n\n\nbase.dists.negativeBinomial.logpmf.factory( r, p )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a negative binomial distribution with number of\n successes until experiment is stopped `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogPMF = base.dists.negativeBinomial.logpmf.factory( 10, 0.5 );\n > var y = mylogPMF( 3.0 )\n ~-3.617\n > y = mylogPMF( 5.0 )\n ~-2.795\n\n","base.dists.negativeBinomial.logpmf.factory":"\nbase.dists.negativeBinomial.logpmf.factory( r, p )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a negative binomial distribution with number of\n successes until experiment is stopped `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogPMF = base.dists.negativeBinomial.logpmf.factory( 10, 0.5 );\n > var y = mylogPMF( 3.0 )\n ~-3.617\n > y = mylogPMF( 5.0 )\n ~-2.795","base.dists.negativeBinomial.mean":"\nbase.dists.negativeBinomial.mean( r, p )\n Returns the expected value of a negative binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n r: integer\n Number of failures until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.negativeBinomial.mean( 100, 0.2 )\n 400\n > v = base.dists.negativeBinomial.mean( 20, 0.5 )\n 20\n\n","base.dists.negativeBinomial.mgf":"\nbase.dists.negativeBinomial.mgf( x, r, p )\n Evaluates the moment-generating function (MGF) for a negative binomial\n distribution with number of successes until experiment is stopped `r` and\n success probability `p` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.negativeBinomial.mgf( 0.05, 20.0, 0.8 )\n ~267.839\n > y = base.dists.negativeBinomial.mgf( 0.1, 20.0, 0.1 )\n ~9.347\n > y = base.dists.negativeBinomial.mgf( 0.5, 10.0, 0.4 )\n ~42822.023\n\n > y = base.dists.negativeBinomial.mgf( 0.1, 0.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.mgf( 0.1, -2.0, 0.5 )\n NaN\n\n > y = base.dists.negativeBinomial.mgf( NaN, 20.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.mgf( 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.mgf( 0.0, 20.0, NaN )\n NaN\n\n > y = base.dists.negativeBinomial.mgf( 0.2, 20, -1.0 )\n NaN\n > y = base.dists.negativeBinomial.mgf( 0.2, 20, 1.5 )\n NaN\n\n\nbase.dists.negativeBinomial.mgf.factory( r, p )\n Returns a function for evaluating the moment-generating function (MGF) of a\n negative binomial distribution with number of successes until experiment is\n stopped `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.negativeBinomial.mgf.factory( 4.3, 0.4 );\n > var y = myMGF( 0.2 )\n ~4.696\n > y = myMGF( 0.4 )\n ~30.83\n\n","base.dists.negativeBinomial.mgf.factory":"\nbase.dists.negativeBinomial.mgf.factory( r, p )\n Returns a function for evaluating the moment-generating function (MGF) of a\n negative binomial distribution with number of successes until experiment is\n stopped `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.negativeBinomial.mgf.factory( 4.3, 0.4 );\n > var y = myMGF( 0.2 )\n ~4.696\n > y = myMGF( 0.4 )\n ~30.83","base.dists.negativeBinomial.mode":"\nbase.dists.negativeBinomial.mode( r, p )\n Returns the mode of a negative binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n r: integer\n Number of failures until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.negativeBinomial.mode( 100, 0.2 )\n 396\n > v = base.dists.negativeBinomial.mode( 20, 0.5 )\n 19\n\n","base.dists.negativeBinomial.NegativeBinomial":"\nbase.dists.negativeBinomial.NegativeBinomial( [r, p] )\n Returns a negative binomial distribution object.\n\n Parameters\n ----------\n r: number (optional)\n Number of successes until experiment is stopped. Must be a positive\n number. Default: `1`.\n\n p: number (optional)\n Success probability. Must be a number between `0` and `1`. Default:\n `0.5`.\n\n Returns\n -------\n nbinomial: Object\n Distribution instance.\n\n nbinomial.r: number\n Number of trials. If set, the value must be a positive number.\n\n nbinomial.p: number\n Success probability. If set, the value must be a number between `0` and\n `1`.\n\n nbinomial.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n nbinomial.mean: number\n Read-only property which returns the expected value.\n\n nbinomial.mode: number\n Read-only property which returns the mode.\n\n nbinomial.skewness: number\n Read-only property which returns the skewness.\n\n nbinomial.stdev: number\n Read-only property which returns the standard deviation.\n\n nbinomial.variance: number\n Read-only property which returns the variance.\n\n nbinomial.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n nbinomial.logpmf: Function\n Evaluates the natural logarithm of the probability mass function (PMF).\n\n nbinomial.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n nbinomial.pmf: Function\n Evaluates the probability mass function (PMF).\n\n nbinomial.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var nbinomial = base.dists.negativeBinomial.NegativeBinomial( 8.0, 0.5 );\n > nbinomial.r\n 8.0\n > nbinomial.p\n 0.5\n > nbinomial.kurtosis\n 0.8125\n > nbinomial.mean\n 8.0\n > nbinomial.mode\n 7.0\n > nbinomial.skewness\n 0.75\n > nbinomial.stdev\n 4.0\n > nbinomial.variance\n 16.0\n > nbinomial.cdf( 2.9 )\n ~0.055\n > nbinomial.logpmf( 3.0 )\n ~-2.837\n > nbinomial.mgf( 0.2 )\n ~36.675\n > nbinomial.pmf( 3.0 )\n ~0.059\n > nbinomial.quantile( 0.8 )\n 11.0\n\n","base.dists.negativeBinomial.pmf":"\nbase.dists.negativeBinomial.pmf( x, r, p )\n Evaluates the probability mass function (PMF) for a negative binomial\n distribution with number of successes until experiment is stopped `r` and\n success probability `p` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated PMF.\n\n Examples\n --------\n > var y = base.dists.negativeBinomial.pmf( 5.0, 20.0, 0.8 )\n ~0.157\n > y = base.dists.negativeBinomial.pmf( 21.0, 20.0, 0.5 )\n ~0.06\n > y = base.dists.negativeBinomial.pmf( 5.0, 10.0, 0.4 )\n ~0.016\n > y = base.dists.negativeBinomial.pmf( 0.0, 10.0, 0.9 )\n ~0.349\n > y = base.dists.negativeBinomial.pmf( 21.0, 15.5, 0.5 )\n ~0.037\n > y = base.dists.negativeBinomial.pmf( 5.0, 7.4, 0.4 )\n ~0.051\n\n > y = base.dists.negativeBinomial.pmf( 2.0, 0.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.pmf( 2.0, -2.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.pmf( 2.0, 20, -1.0 )\n NaN\n > y = base.dists.negativeBinomial.pmf( 2.0, 20, 1.5 )\n NaN\n\n > y = base.dists.negativeBinomial.pmf( NaN, 20.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.pmf( 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.pmf( 0.0, 20.0, NaN )\n NaN\n\n\nbase.dists.negativeBinomial.pmf.factory( r, p )\n Returns a function for evaluating the probability mass function (PMF) of a\n negative binomial distribution with number of successes until experiment is\n stopped `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var myPMF = base.dists.negativeBinomial.pmf.factory( 10, 0.5 );\n > var y = myPMF( 3.0 )\n ~0.027\n > y = myPMF( 5.0 )\n ~0.061\n\n","base.dists.negativeBinomial.pmf.factory":"\nbase.dists.negativeBinomial.pmf.factory( r, p )\n Returns a function for evaluating the probability mass function (PMF) of a\n negative binomial distribution with number of successes until experiment is\n stopped `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var myPMF = base.dists.negativeBinomial.pmf.factory( 10, 0.5 );\n > var y = myPMF( 3.0 )\n ~0.027\n > y = myPMF( 5.0 )\n ~0.061","base.dists.negativeBinomial.quantile":"\nbase.dists.negativeBinomial.quantile( k, r, p )\n Evaluates the quantile function for a negative binomial distribution with\n number of successes until experiment is stopped `r` and success probability\n `p` at a probability `k`.\n\n If provided a `k` outside of `[0,1]`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n k: number\n Input probability.\n\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.negativeBinomial.quantile( 0.9, 20.0, 0.2 )\n 106\n > y = base.dists.negativeBinomial.quantile( 0.9, 20.0, 0.8 )\n 8\n > y = base.dists.negativeBinomial.quantile( 0.5, 10.0, 0.4 )\n 14\n > y = base.dists.negativeBinomial.quantile( 0.0, 10.0, 0.9 )\n 0\n\n > y = base.dists.negativeBinomial.quantile( 1.1, 20.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.quantile( -0.1, 20.0, 0.5 )\n NaN\n\n > y = base.dists.negativeBinomial.quantile( 21.0, 15.5, 0.5 )\n 12\n > y = base.dists.negativeBinomial.quantile( 5.0, 7.4, 0.4 )\n 10\n\n > y = base.dists.negativeBinomial.quantile( 0.5, 0.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.quantile( 0.5, -2.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.quantile( 0.3, 20.0, -1.0 )\n NaN\n > y = base.dists.negativeBinomial.quantile( 0.3, 20.0, 1.5 )\n NaN\n\n > y = base.dists.negativeBinomial.quantile( NaN, 20.0, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.quantile( 0.3, NaN, 0.5 )\n NaN\n > y = base.dists.negativeBinomial.quantile( 0.3, 20.0, NaN )\n NaN\n\n\nbase.dists.negativeBinomial.quantile.factory( r, p )\n Returns a function for evaluating the quantile function of a negative\n binomial distribution with number of successes until experiment is stopped\n `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.negativeBinomial.quantile.factory( 10.0, 0.5 );\n > var y = myQuantile( 0.1 )\n 5\n > y = myQuantile( 0.9 )\n 16\n\n","base.dists.negativeBinomial.quantile.factory":"\nbase.dists.negativeBinomial.quantile.factory( r, p )\n Returns a function for evaluating the quantile function of a negative\n binomial distribution with number of successes until experiment is stopped\n `r` and success probability `p`.\n\n Parameters\n ----------\n r: number\n Number of successes until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.negativeBinomial.quantile.factory( 10.0, 0.5 );\n > var y = myQuantile( 0.1 )\n 5\n > y = myQuantile( 0.9 )\n 16","base.dists.negativeBinomial.skewness":"\nbase.dists.negativeBinomial.skewness( r, p )\n Returns the skewness of a negative binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n r: integer\n Number of failures until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.negativeBinomial.skewness( 100, 0.2 )\n ~0.201\n > v = base.dists.negativeBinomial.skewness( 20, 0.5 )\n ~0.474\n\n","base.dists.negativeBinomial.stdev":"\nbase.dists.negativeBinomial.stdev( r, p )\n Returns the standard deviation of a negative binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n r: integer\n Number of failures until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.negativeBinomial.stdev( 100, 0.2 )\n ~44.721\n > v = base.dists.negativeBinomial.stdev( 20, 0.5 )\n ~6.325\n\n","base.dists.negativeBinomial.variance":"\nbase.dists.negativeBinomial.variance( r, p )\n Returns the variance of a negative binomial distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a `r` which is not a positive number, the function returns\n `NaN`.\n\n If provided a success probability `p` outside of `[0,1]`, the function\n returns `NaN`.\n\n Parameters\n ----------\n r: integer\n Number of failures until experiment is stopped.\n\n p: number\n Success probability.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.negativeBinomial.variance( 100, 0.2 )\n 2000.0\n > v = base.dists.negativeBinomial.variance( 20, 0.5 )\n 40.0\n\n","base.dists.normal.cdf":"\nbase.dists.normal.cdf( x, μ, σ )\n Evaluates the cumulative distribution function (CDF) for a normal\n distribution with mean `μ` and standard deviation `σ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.normal.cdf( 2.0, 0.0, 1.0 )\n ~0.977\n > y = base.dists.normal.cdf( -1.0, -1.0, 2.0 )\n 0.5\n > y = base.dists.normal.cdf( -1.0, 4.0, 2.0 )\n ~0.006\n > y = base.dists.normal.cdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.normal.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.normal.cdf( 0.0, 0.0, NaN )\n NaN\n\n // Negative standard deviation:\n > y = base.dists.normal.cdf( 2.0, 0.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `μ` when `σ = 0.0`:\n > y = base.dists.normal.cdf( 2.0, 8.0, 0.0 )\n 0.0\n > y = base.dists.normal.cdf( 8.0, 8.0, 0.0 )\n 1.0\n > y = base.dists.normal.cdf( 10.0, 8.0, 0.0 )\n 1.0\n\n\nbase.dists.normal.cdf.factory( μ, σ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a normal distribution with mean `μ` and standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.normal.cdf.factory( 10.0, 2.0 );\n > var y = myCDF( 10.0 )\n 0.5\n\n","base.dists.normal.cdf.factory":"\nbase.dists.normal.cdf.factory( μ, σ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a normal distribution with mean `μ` and standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.normal.cdf.factory( 10.0, 2.0 );\n > var y = myCDF( 10.0 )\n 0.5","base.dists.normal.entropy":"\nbase.dists.normal.entropy( μ, σ )\n Returns the differential entropy of a normal distribution with mean `μ` and\n standard deviation `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var y = base.dists.normal.entropy( 0.0, 1.0 )\n ~1.419\n > y = base.dists.normal.entropy( 4.0, 3.0 )\n ~2.518\n > y = base.dists.normal.entropy( NaN, 1.0 )\n NaN\n > y = base.dists.normal.entropy( 0.0, NaN )\n NaN\n > y = base.dists.normal.entropy( 0.0, 0.0 )\n NaN\n\n","base.dists.normal.kurtosis":"\nbase.dists.normal.kurtosis( μ, σ )\n Returns the excess kurtosis of a normal distribution with mean `μ` and\n standard deviation `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var y = base.dists.normal.kurtosis( 0.0, 1.0 )\n 0.0\n > y = base.dists.normal.kurtosis( 4.0, 3.0 )\n 0.0\n > y = base.dists.normal.kurtosis( NaN, 1.0 )\n NaN\n > y = base.dists.normal.kurtosis( 0.0, NaN )\n NaN\n > y = base.dists.normal.kurtosis( 0.0, 0.0 )\n NaN\n\n","base.dists.normal.logcdf":"\nbase.dists.normal.logcdf( x, μ, σ )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a normal distribution with mean `μ` and standard deviation `σ` at\n a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Evaluated logcdf.\n\n Examples\n --------\n > var y = base.dists.normal.logcdf( 2.0, 0.0, 1.0 )\n ~-0.023\n > y = base.dists.normal.logcdf( -1.0, 4.0, 2.0 )\n ~-5.082\n > y = base.dists.normal.logcdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.normal.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.normal.logcdf( 0.0, 0.0, NaN )\n NaN\n\n // Negative standard deviation:\n > y = base.dists.normal.logcdf( 2.0, 0.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `μ` when `σ = 0.0`:\n > y = base.dists.normal.logcdf( 2.0, 8.0, 0.0 )\n -Infinity\n > y = base.dists.normal.logcdf( 8.0, 8.0, 0.0 )\n 0.0\n\n\nbase.dists.normal.logcdf.factory( μ, σ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a normal distribution with mean `μ` and\n standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.normal.logcdf.factory( 10.0, 2.0 );\n > var y = mylogcdf( 10.0 )\n ~-0.693\n\n","base.dists.normal.logcdf.factory":"\nbase.dists.normal.logcdf.factory( μ, σ )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a normal distribution with mean `μ` and\n standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.normal.logcdf.factory( 10.0, 2.0 );\n > var y = mylogcdf( 10.0 )\n ~-0.693","base.dists.normal.logpdf":"\nbase.dists.normal.logpdf( x, μ, σ )\n Evaluates the natural logarithm of the probability density function (PDF)\n for a normal distribution with mean `μ` and standard deviation `σ` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.normal.logpdf( 2.0, 0.0, 1.0 )\n ~-2.919\n > y = base.dists.normal.logpdf( -1.0, 4.0, 2.0 )\n ~-4.737\n > y = base.dists.normal.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.normal.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.normal.logpdf( 0.0, 0.0, NaN )\n NaN\n\n // Negative standard deviation:\n > y = base.dists.normal.logpdf( 2.0, 0.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `μ` when `σ = 0.0`:\n > y = base.dists.normal.logpdf( 2.0, 8.0, 0.0 )\n -Infinity\n > y = base.dists.normal.logpdf( 8.0, 8.0, 0.0 )\n Infinity\n\n\nbase.dists.normal.logpdf.factory( μ, σ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a normal distribution with mean `μ` and standard\n deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var myLogPDF = base.dists.normal.logpdf.factory( 10.0, 2.0 );\n > var y = myLogPDF( 10.0 )\n ~-1.612\n\n","base.dists.normal.logpdf.factory":"\nbase.dists.normal.logpdf.factory( μ, σ )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a normal distribution with mean `μ` and standard\n deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var myLogPDF = base.dists.normal.logpdf.factory( 10.0, 2.0 );\n > var y = myLogPDF( 10.0 )\n ~-1.612","base.dists.normal.mean":"\nbase.dists.normal.mean( μ, σ )\n Returns the expected value of a normal distribution with mean `μ` and\n standard deviation `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var y = base.dists.normal.mean( 0.0, 1.0 )\n 0.0\n > y = base.dists.normal.mean( 4.0, 2.0 )\n 4.0\n > y = base.dists.normal.mean( NaN, 1.0 )\n NaN\n > y = base.dists.normal.mean( 0.0, NaN )\n NaN\n > y = base.dists.normal.mean( 0.0, 0.0 )\n NaN\n\n","base.dists.normal.median":"\nbase.dists.normal.median( μ, σ )\n Returns the median of a normal distribution with mean `μ` and standard\n deviation `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var y = base.dists.normal.median( 0.0, 1.0 )\n 0.0\n > y = base.dists.normal.median( 4.0, 2.0 )\n 4.0\n > y = base.dists.normal.median( NaN, 1.0 )\n NaN\n > y = base.dists.normal.median( 0.0, NaN )\n NaN\n > y = base.dists.normal.median( 0.0, 0.0 )\n NaN\n\n","base.dists.normal.mgf":"\nbase.dists.normal.mgf( x, μ, σ )\n Evaluates the moment-generating function (MGF) for a normal distribution\n with mean `μ` and standard deviation `σ` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.normal.mgf( 2.0, 0.0, 1.0 )\n ~7.389\n > y = base.dists.normal.mgf( 0.0, 0.0, 1.0 )\n 1.0\n > y = base.dists.normal.mgf( -1.0, 4.0, 2.0 )\n ~0.1353\n > y = base.dists.normal.mgf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.normal.mgf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.normal.mgf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.normal.mgf( 2.0, 0.0, 0.0 )\n NaN\n\n\nbase.dists.normal.mgf.factory( μ, σ )\n Returns a function for evaluating the moment-generating function (MGF) of a\n normal distribution with mean `μ` and standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.normal.mgf.factory( 4.0, 2.0 );\n > var y = myMGF( 1.0 )\n ~403.429\n > y = myMGF( 0.5 )\n ~12.182\n\n","base.dists.normal.mgf.factory":"\nbase.dists.normal.mgf.factory( μ, σ )\n Returns a function for evaluating the moment-generating function (MGF) of a\n normal distribution with mean `μ` and standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.normal.mgf.factory( 4.0, 2.0 );\n > var y = myMGF( 1.0 )\n ~403.429\n > y = myMGF( 0.5 )\n ~12.182","base.dists.normal.mode":"\nbase.dists.normal.mode( μ, σ )\n Returns the mode of a normal distribution with mean `μ` and standard\n deviation `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var y = base.dists.normal.mode( 0.0, 1.0 )\n 0.0\n > y = base.dists.normal.mode( 4.0, 2.0 )\n 4.0\n > y = base.dists.normal.mode( NaN, 1.0 )\n NaN\n > y = base.dists.normal.mode( 0.0, NaN )\n NaN\n > y = base.dists.normal.mode( 0.0, 0.0 )\n NaN\n\n","base.dists.normal.Normal":"\nbase.dists.normal.Normal( [μ, σ] )\n Returns a normal distribution object.\n\n Parameters\n ----------\n μ: number (optional)\n Mean parameter. Default: `0.0`.\n\n σ: number (optional)\n Standard deviation. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n normal: Object\n Distribution instance.\n\n normal.mu: number\n Mean parameter.\n\n normal.sigma: number\n Standard deviation. If set, the value must be greater than `0`.\n\n normal.entropy: number\n Read-only property which returns the differential entropy.\n\n normal.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n normal.mean: number\n Read-only property which returns the expected value.\n\n normal.median: number\n Read-only property which returns the median.\n\n normal.mode: number\n Read-only property which returns the mode.\n\n normal.skewness: number\n Read-only property which returns the skewness.\n\n normal.stdev: number\n Read-only property which returns the standard deviation.\n\n normal.variance: number\n Read-only property which returns the variance.\n\n normal.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n normal.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n normal.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n normal.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n normal.pdf: Function\n Evaluates the probability density function (PDF).\n\n normal.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var normal = base.dists.normal.Normal( -2.0, 3.0 );\n > normal.mu\n -2.0\n > normal.sigma\n 3.0\n > normal.entropy\n ~2.518\n > normal.kurtosis\n 0.0\n > normal.mean\n -2.0\n > normal.median\n -2.0\n > normal.mode\n -2.0\n > normal.skewness\n 0.0\n > normal.stdev\n 3.0\n > normal.variance\n 9.0\n > normal.cdf( 0.8 )\n ~0.825\n > normal.logcdf( 0.8 )\n ~-0.193\n > normal.logpdf( 2.0 )\n ~-2.9\n > normal.mgf( 0.2 )\n ~0.803\n > normal.pdf( 2.0 )\n ~0.055\n > normal.quantile( 0.9 )\n ~1.845\n\n","base.dists.normal.pdf":"\nbase.dists.normal.pdf( x, μ, σ )\n Evaluates the probability density function (PDF) for a normal distribution\n with mean `μ` and standard deviation `σ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.normal.pdf( 2.0, 0.0, 1.0 )\n ~0.054\n > y = base.dists.normal.pdf( -1.0, 4.0, 2.0 )\n ~0.009\n > y = base.dists.normal.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.normal.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.normal.pdf( 0.0, 0.0, NaN )\n NaN\n\n // Negative standard deviation:\n > y = base.dists.normal.pdf( 2.0, 0.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `μ` when `σ = 0.0`:\n > y = base.dists.normal.pdf( 2.0, 8.0, 0.0 )\n 0.0\n > y = base.dists.normal.pdf( 8.0, 8.0, 0.0 )\n infinity\n\n\nbase.dists.normal.pdf.factory( μ, σ )\n Returns a function for evaluating the probability density function (PDF) of\n a normal distribution with mean `μ` and standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.normal.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n ~0.199\n\n","base.dists.normal.pdf.factory":"\nbase.dists.normal.pdf.factory( μ, σ )\n Returns a function for evaluating the probability density function (PDF) of\n a normal distribution with mean `μ` and standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.normal.pdf.factory( 10.0, 2.0 );\n > var y = myPDF( 10.0 )\n ~0.199","base.dists.normal.quantile":"\nbase.dists.normal.quantile( p, μ, σ )\n Evaluates the quantile function for a normal distribution with mean `μ` and\n standard deviation `σ` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.normal.quantile( 0.8, 0.0, 1.0 )\n ~0.842\n > y = base.dists.normal.quantile( 0.5, 4.0, 2.0 )\n 4\n\n > y = base.dists.normal.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.normal.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.normal.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.normal.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.normal.quantile( 0.0, 0.0, NaN )\n NaN\n\n // Negative standard deviation:\n > y = base.dists.normal.quantile( 0.5, 0.0, -1.0 )\n NaN\n\n // Degenerate distribution centered at `μ` when `σ = 0.0`:\n > y = base.dists.normal.quantile( 0.3, 8.0, 0.0 )\n 8.0\n > y = base.dists.normal.quantile( 0.9, 8.0, 0.0 )\n 8.0\n\n\nbase.dists.normal.quantile.factory( μ, σ )\n Returns a function for evaluating the quantile function\n of a normal distribution with mean `μ` and standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.normal.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0\n\n","base.dists.normal.quantile.factory":"\nbase.dists.normal.quantile.factory( μ, σ )\n Returns a function for evaluating the quantile function\n of a normal distribution with mean `μ` and standard deviation `σ`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.normal.quantile.factory( 10.0, 2.0 );\n > var y = myQuantile( 0.5 )\n 10.0","base.dists.normal.skewness":"\nbase.dists.normal.skewness( μ, σ )\n Returns the skewness of a normal distribution with mean `μ` and standard\n deviation `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var y = base.dists.normal.skewness( 0.0, 1.0 )\n 0.0\n > y = base.dists.normal.skewness( 4.0, 3.0 )\n 0.0\n > y = base.dists.normal.skewness( NaN, 1.0 )\n NaN\n > y = base.dists.normal.skewness( 0.0, NaN )\n NaN\n > y = base.dists.normal.skewness( 0.0, 0.0 )\n NaN\n\n","base.dists.normal.stdev":"\nbase.dists.normal.stdev( μ, σ )\n Returns the standard deviation of a normal distribution with mean `μ` and\n standard deviation `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var y = base.dists.normal.stdev( 0.0, 1.0 )\n 1.0\n > y = base.dists.normal.stdev( 4.0, 3.0 )\n 3.0\n > y = base.dists.normal.stdev( NaN, 1.0 )\n NaN\n > y = base.dists.normal.stdev( 0.0, NaN )\n NaN\n > y = base.dists.normal.stdev( 0.0, 0.0 )\n NaN\n\n","base.dists.normal.variance":"\nbase.dists.normal.variance( μ, σ )\n Returns the variance of a normal distribution with mean `μ` and standard\n deviation `σ`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n μ: number\n Location parameter.\n\n σ: number\n Standard deviation.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var y = base.dists.normal.variance( 0.0, 1.0 )\n 1.0\n > y = base.dists.normal.variance( 4.0, 3.0 )\n 9.0\n > y = base.dists.normal.variance( NaN, 1.0 )\n NaN\n > y = base.dists.normal.variance( 0.0, NaN )\n NaN\n > y = base.dists.normal.variance( 0.0, 0.0 )\n NaN\n\n","base.dists.pareto1.cdf":"\nbase.dists.pareto1.cdf( x, α, β )\n Evaluates the cumulative distribution function (CDF) for a Pareto (Type I)\n distribution with shape parameter `α` and scale parameter `β` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.pareto1.cdf( 2.0, 1.0, 1.0 )\n 0.5\n > y = base.dists.pareto1.cdf( 5.0, 2.0, 4.0 )\n ~0.36\n > y = base.dists.pareto1.cdf( 4.0, 2.0, 2.0 )\n 0.75\n > y = base.dists.pareto1.cdf( 1.9, 2.0, 2.0 )\n 0.0\n > y = base.dists.pareto1.cdf( PINF, 4.0, 2.0 )\n 1.0\n\n > y = base.dists.pareto1.cdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.pareto1.cdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.pareto1.cdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.pareto1.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.pareto1.cdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.pareto1.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Pareto (Type I) distribution with shape parameter `α` and scale\n parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.pareto1.cdf.factory( 10.0, 2.0 );\n > var y = myCDF( 3.0 )\n ~0.983\n > y = myCDF( 2.5 )\n ~0.893\n\n","base.dists.pareto1.cdf.factory":"\nbase.dists.pareto1.cdf.factory( α, β )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Pareto (Type I) distribution with shape parameter `α` and scale\n parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.pareto1.cdf.factory( 10.0, 2.0 );\n > var y = myCDF( 3.0 )\n ~0.983\n > y = myCDF( 2.5 )\n ~0.893","base.dists.pareto1.entropy":"\nbase.dists.pareto1.entropy( α, β )\n Returns the differential entropy of a Pareto (Type I) distribution\n (in nats).\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Differential entropy.\n\n Examples\n --------\n > var v = base.dists.pareto1.entropy( 0.8, 1.0 )\n ~2.473\n > v = base.dists.pareto1.entropy( 4.0, 12.0 )\n ~2.349\n > v = base.dists.pareto1.entropy( 8.0, 2.0 )\n ~-0.261\n\n","base.dists.pareto1.kurtosis":"\nbase.dists.pareto1.kurtosis( α, β )\n Returns the excess kurtosis of a Pareto (Type I) distribution.\n\n If `α <= 4` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.pareto1.kurtosis( 5.0, 1.0 )\n ~70.8\n > v = base.dists.pareto1.kurtosis( 4.5, 12.0 )\n ~146.444\n > v = base.dists.pareto1.kurtosis( 8.0, 2.0 )\n ~19.725\n\n","base.dists.pareto1.logcdf":"\nbase.dists.pareto1.logcdf( x, α, β )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a Pareto (Type I) distribution with shape parameter `α` and scale\n parameter `β` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.pareto1.logcdf( 2.0, 1.0, 1.0 )\n ~-0.693\n > y = base.dists.pareto1.logcdf( 5.0, 2.0, 4.0 )\n ~-1.022\n > y = base.dists.pareto1.logcdf( 4.0, 2.0, 2.0 )\n ~-0.288\n > y = base.dists.pareto1.logcdf( 1.9, 2.0, 2.0 )\n -Infinity\n > y = base.dists.pareto1.logcdf( PINF, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.pareto1.logcdf( 2.0, -1.0, 0.5 )\n NaN\n > y = base.dists.pareto1.logcdf( 2.0, 0.5, -1.0 )\n NaN\n\n > y = base.dists.pareto1.logcdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.pareto1.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.pareto1.logcdf( 0.0, 1.0, NaN )\n NaN\n\n\nbase.dists.pareto1.logcdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a Pareto (Type I) distribution with shape\n parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogCDF = base.dists.pareto1.logcdf.factory( 10.0, 2.0 );\n > var y = mylogCDF( 3.0 )\n ~-0.017\n > y = mylogCDF( 2.5 )\n ~-0.114\n\n","base.dists.pareto1.logcdf.factory":"\nbase.dists.pareto1.logcdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a Pareto (Type I) distribution with shape\n parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogCDF = base.dists.pareto1.logcdf.factory( 10.0, 2.0 );\n > var y = mylogCDF( 3.0 )\n ~-0.017\n > y = mylogCDF( 2.5 )\n ~-0.114","base.dists.pareto1.logpdf":"\nbase.dists.pareto1.logpdf( x, α, β )\n Evaluates the natural logarithm of the probability density function (PDF)\n for a Pareto (Type I) distribution with shape parameter `α` and scale\n parameter `β` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.pareto1.logpdf( 4.0, 1.0, 1.0 )\n ~-2.773\n > y = base.dists.pareto1.logpdf( 20.0, 1.0, 10.0 )\n ~-3.689\n > y = base.dists.pareto1.logpdf( 7.0, 2.0, 6.0 )\n ~-1.561\n > y = base.dists.pareto1.logpdf( 7.0, 6.0, 3.0 )\n ~-5.238\n > y = base.dists.pareto1.logpdf( 1.0, 4.0, 2.0 )\n -Infinity\n > y = base.dists.pareto1.logpdf( 1.5, 4.0, 2.0 )\n -Infinity\n\n > y = base.dists.pareto1.logpdf( 0.5, -1.0, 0.5 )\n NaN\n > y = base.dists.pareto1.logpdf( 0.5, 0.5, -1.0 )\n NaN\n\n > y = base.dists.pareto1.logpdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.pareto1.logpdf( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.pareto1.logpdf( 0.5, 1.0, NaN )\n NaN\n\n\nbase.dists.pareto1.logpdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a Pareto (Type I) distribution with shape\n parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.pareto1.logpdf.factory( 0.5, 0.5 );\n > var y = mylogPDF( 0.8 )\n ~-0.705\n > y = mylogPDF( 2.0 )\n ~-2.079\n\n","base.dists.pareto1.logpdf.factory":"\nbase.dists.pareto1.logpdf.factory( α, β )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a Pareto (Type I) distribution with shape\n parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.pareto1.logpdf.factory( 0.5, 0.5 );\n > var y = mylogPDF( 0.8 )\n ~-0.705\n > y = mylogPDF( 2.0 )\n ~-2.079","base.dists.pareto1.mean":"\nbase.dists.pareto1.mean( α, β )\n Returns the expected value of a Pareto (Type I) distribution.\n\n If `0 < α <= 1`, the function returns `Infinity`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.pareto1.mean( 0.8, 1.0 )\n Infinity\n > v = base.dists.pareto1.mean( 4.0, 12.0 )\n 16.0\n > v = base.dists.pareto1.mean( 8.0, 2.0 )\n ~2.286\n\n","base.dists.pareto1.median":"\nbase.dists.pareto1.median( α, β )\n Returns the median of a Pareto (Type I) distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.pareto1.median( 0.8, 1.0 )\n ~2.378\n > v = base.dists.pareto1.median( 4.0, 12.0 )\n ~14.27\n > v = base.dists.pareto1.median( 8.0, 2.0 )\n ~2.181\n\n","base.dists.pareto1.mode":"\nbase.dists.pareto1.mode( α, β )\n Returns the mode of a Pareto (Type I) distribution.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.pareto1.mode( 0.8, 1.0 )\n 1.0\n > v = base.dists.pareto1.mode( 4.0, 12.0 )\n 12.0\n > v = base.dists.pareto1.mode( 8.0, 2.0 )\n 2.0\n\n","base.dists.pareto1.Pareto1":"\nbase.dists.pareto1.Pareto1( [α, β] )\n Returns a Pareto (Type I) distribution object.\n\n Parameters\n ----------\n α: number (optional)\n Shape parameter. Must be greater than `0`. Default: `1.0`.\n\n β: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n pareto1: Object\n Distribution instance.\n\n pareto1.alpha: number\n Shape parameter. If set, the value must be greater than `0`.\n\n pareto1.beta: number\n Scale parameter. If set, the value must be greater than `0`.\n\n pareto1.entropy: number\n Read-only property which returns the differential entropy.\n\n pareto1.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n pareto1.mean: number\n Read-only property which returns the expected value.\n\n pareto1.median: number\n Read-only property which returns the median.\n\n pareto1.mode: number\n Read-only property which returns the mode.\n\n pareto1.skewness: number\n Read-only property which returns the skewness.\n\n pareto1.variance: number\n Read-only property which returns the variance.\n\n pareto1.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n pareto1.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (logCDF).\n\n pareto1.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (logPDF).\n\n pareto1.pdf: Function\n Evaluates the probability density function (PDF).\n\n pareto1.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var pareto1 = base.dists.pareto1.Pareto1( 6.0, 5.0 );\n > pareto1.alpha\n 6.0\n > pareto1.beta\n 5.0\n > pareto1.entropy\n ~0.984\n > pareto1.kurtosis\n ~35.667\n > pareto1.mean\n 6.0\n > pareto1.median\n ~5.612\n > pareto1.mode\n 5.0\n > pareto1.skewness\n ~3.81\n > pareto1.variance\n 1.5\n > pareto1.cdf( 7.0 )\n ~0.867\n > pareto1.logcdf( 7.0 )\n ~-0.142\n > pareto1.logpdf( 5.0 )\n ~0.182\n > pareto1.pdf( 5.0 )\n 1.2\n > pareto1.quantile( 0.8 )\n ~6.538\n\n","base.dists.pareto1.pdf":"\nbase.dists.pareto1.pdf( x, α, β )\n Evaluates the probability density function (PDF) for a Pareto (Type I)\n distribution with shape parameter `α` and scale parameter `β` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.pareto1.pdf( 4.0, 1.0, 1.0 )\n ~0.063\n > y = base.dists.pareto1.pdf( 20.0, 1.0, 10.0 )\n 0.025\n > y = base.dists.pareto1.pdf( 7.0, 2.0, 6.0 )\n ~0.21\n > y = base.dists.pareto1.pdf( 7.0, 6.0, 3.0 )\n ~0.005\n > y = base.dists.pareto1.pdf( 1.0, 4.0, 2.0 )\n 0.0\n > y = base.dists.pareto1.pdf( 1.5, 4.0, 2.0 )\n 0.0\n\n > y = base.dists.pareto1.pdf( 0.5, -1.0, 0.5 )\n NaN\n > y = base.dists.pareto1.pdf( 0.5, 0.5, -1.0 )\n NaN\n\n > y = base.dists.pareto1.pdf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.pareto1.pdf( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.pareto1.pdf( 0.5, 1.0, NaN )\n NaN\n\n\nbase.dists.pareto1.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF) of\n a Pareto (Type I) distribution with shape parameter `α` and scale parameter\n `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.pareto1.pdf.factory( 0.5, 0.5 );\n > var y = myPDF( 0.8 )\n ~0.494\n > y = myPDF( 2.0 )\n ~0.125\n\n","base.dists.pareto1.pdf.factory":"\nbase.dists.pareto1.pdf.factory( α, β )\n Returns a function for evaluating the probability density function (PDF) of\n a Pareto (Type I) distribution with shape parameter `α` and scale parameter\n `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.pareto1.pdf.factory( 0.5, 0.5 );\n > var y = myPDF( 0.8 )\n ~0.494\n > y = myPDF( 2.0 )\n ~0.125","base.dists.pareto1.quantile":"\nbase.dists.pareto1.quantile( p, α, β )\n Evaluates the quantile function for a Pareto (Type I) distribution with\n shape parameter `α` and scale parameter `β` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.pareto1.quantile( 0.8, 2.0, 1.0 )\n ~2.236\n > y = base.dists.pareto1.quantile( 0.8, 1.0, 10.0 )\n ~50.0\n > y = base.dists.pareto1.quantile( 0.1, 1.0, 10.0 )\n ~11.111\n\n > y = base.dists.pareto1.quantile( 1.1, 1.0, 1.0 )\n NaN\n > y = base.dists.pareto1.quantile( -0.2, 1.0, 1.0 )\n NaN\n\n > y = base.dists.pareto1.quantile( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.pareto1.quantile( 0.5, NaN, 1.0 )\n NaN\n > y = base.dists.pareto1.quantile( 0.5, 1.0, NaN )\n NaN\n\n > y = base.dists.pareto1.quantile( 0.5, -1.0, 1.0 )\n NaN\n > y = base.dists.pareto1.quantile( 0.5, 1.0, -1.0 )\n NaN\n\n\nbase.dists.pareto1.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of a Pareto (Type I)\n distribution with shape parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.pareto1.quantile.factory( 2.5, 0.5 );\n > var y = myQuantile( 0.5 )\n ~0.66\n > y = myQuantile( 0.8 )\n ~0.952\n\n","base.dists.pareto1.quantile.factory":"\nbase.dists.pareto1.quantile.factory( α, β )\n Returns a function for evaluating the quantile function of a Pareto (Type I)\n distribution with shape parameter `α` and scale parameter `β`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.pareto1.quantile.factory( 2.5, 0.5 );\n > var y = myQuantile( 0.5 )\n ~0.66\n > y = myQuantile( 0.8 )\n ~0.952","base.dists.pareto1.skewness":"\nbase.dists.pareto1.skewness( α, β )\n Returns the skewness of a Pareto (Type I) distribution.\n\n If `α <= 3` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.pareto1.skewness( 3.5, 1.0 )\n ~11.784\n > v = base.dists.pareto1.skewness( 4.0, 12.0 )\n ~7.071\n > v = base.dists.pareto1.skewness( 8.0, 2.0 )\n ~3.118\n\n","base.dists.pareto1.stdev":"\nbase.dists.pareto1.stdev( α, β )\n Returns the standard deviation of a Pareto (Type I) distribution.\n\n If `0 < α <= 2` and `β > 0`, the function returns positive infinity.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.pareto1.stdev( 0.8, 1.0 )\n Infinity\n > v = base.dists.pareto1.stdev( 4.0, 12.0 )\n ~5.657\n > v = base.dists.pareto1.stdev( 8.0, 2.0 )\n ~0.33\n\n","base.dists.pareto1.variance":"\nbase.dists.pareto1.variance( α, β )\n Returns the variance of a Pareto (Type I) distribution.\n\n If `0 < α <= 2` and `β > 0`, the function returns positive infinity.\n\n If `α <= 0` or `β <= 0`, the function returns `NaN`.\n\n If `α` or `β` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n α: number\n Shape parameter.\n\n β: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.pareto1.variance( 0.8, 1.0 )\n Infinity\n > v = base.dists.pareto1.variance( 4.0, 12.0 )\n 32.0\n > v = base.dists.pareto1.variance( 8.0, 2.0 )\n ~0.109\n\n","base.dists.poisson.cdf":"\nbase.dists.poisson.cdf( x, λ )\n Evaluates the cumulative distribution function (CDF) for a Poisson\n distribution with mean parameter `λ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.poisson.cdf( 2.0, 0.5 )\n ~0.986\n > y = base.dists.poisson.cdf( 2.0, 10.0 )\n ~0.003\n > y = base.dists.poisson.cdf( -1.0, 4.0 )\n 0.0\n > y = base.dists.poisson.cdf( NaN, 1.0 )\n NaN\n > y = base.dists.poisson.cdf( 0.0, NaN )\n NaN\n\n // Negative mean parameter:\n > y = base.dists.poisson.cdf( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution at `λ = 0`:\n > y = base.dists.poisson.cdf( -2.0, 0.0 )\n 0.0\n > y = base.dists.poisson.cdf( 0.0, 0.0 )\n 1.0\n > y = base.dists.poisson.cdf( 10.0, 0.0 )\n 1.0\n\n\nbase.dists.poisson.cdf.factory( λ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Poisson distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.poisson.cdf.factory( 5.0 );\n > var y = mycdf( 3.0 )\n ~0.265\n > y = mycdf( 8.0 )\n ~0.932\n\n","base.dists.poisson.cdf.factory":"\nbase.dists.poisson.cdf.factory( λ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Poisson distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.poisson.cdf.factory( 5.0 );\n > var y = mycdf( 3.0 )\n ~0.265\n > y = mycdf( 8.0 )\n ~0.932","base.dists.poisson.entropy":"\nbase.dists.poisson.entropy( λ )\n Returns the entropy of a Poisson distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.poisson.entropy( 11.0 )\n ~2.61\n > v = base.dists.poisson.entropy( 4.5 )\n ~2.149\n\n","base.dists.poisson.kurtosis":"\nbase.dists.poisson.kurtosis( λ )\n Returns the excess kurtosis of a Poisson distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a non-positive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.poisson.kurtosis( 11.0 )\n ~0.091\n > v = base.dists.poisson.kurtosis( 4.5 )\n ~0.222\n\n","base.dists.poisson.logpmf":"\nbase.dists.poisson.logpmf( x, λ )\n Evaluates the natural logarithm of the probability mass function (PMF) for a\n Poisson distribution with mean parameter `λ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Evaluated logPMF.\n\n Examples\n --------\n > var y = base.dists.poisson.logpmf( 4.0, 3.0 )\n ~-1.784\n > y = base.dists.poisson.logpmf( 1.0, 3.0 )\n ~-1.901\n > y = base.dists.poisson.logpmf( -1.0, 2.0 )\n -Infinity\n > y = base.dists.poisson.logpmf( 0.0, NaN )\n NaN\n > y = base.dists.poisson.logpmf( NaN, 0.5 )\n NaN\n\n // Negative mean parameter:\n > y = base.dists.poisson.logpmf( 2.0, -0.5 )\n NaN\n\n // Degenerate distribution at `λ = 0`:\n > y = base.dists.poisson.logpmf( 2.0, 0.0 )\n -Infinity\n > y = base.dists.poisson.logpmf( 0.0, 0.0 )\n 0.0\n\n\nbase.dists.poisson.logpmf.factory( λ )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a Poisson distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogpmf = base.dists.poisson.logpmf.factory( 1.0 );\n > var y = mylogpmf( 3.0 )\n ~-2.792\n > y = mylogpmf( 1.0 )\n ~-1.0\n\n","base.dists.poisson.logpmf.factory":"\nbase.dists.poisson.logpmf.factory( λ )\n Returns a function for evaluating the natural logarithm of the probability\n mass function (PMF) of a Poisson distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n logpmf: Function\n Logarithm of probability mass function (PMF).\n\n Examples\n --------\n > var mylogpmf = base.dists.poisson.logpmf.factory( 1.0 );\n > var y = mylogpmf( 3.0 )\n ~-2.792\n > y = mylogpmf( 1.0 )\n ~-1.0","base.dists.poisson.mean":"\nbase.dists.poisson.mean( λ )\n Returns the expected value of a Poisson distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.poisson.mean( 11.0 )\n 11.0\n > v = base.dists.poisson.mean( 4.5 )\n 4.5\n\n","base.dists.poisson.median":"\nbase.dists.poisson.median( λ )\n Returns the median of a Poisson distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: integer\n Median.\n\n Examples\n --------\n > var v = base.dists.poisson.median( 11.0 )\n 11\n > v = base.dists.poisson.median( 4.5 )\n 4\n\n","base.dists.poisson.mgf":"\nbase.dists.poisson.mgf( x, λ )\n Evaluates the moment-generating function (MGF) for a Poisson distribution\n with mean parameter `λ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.poisson.mgf( 1.0, 1.5 )\n ~13.163\n > y = base.dists.poisson.mgf( 0.5, 0.5 )\n ~1.383\n > y = base.dists.poisson.mgf( NaN, 0.5 )\n NaN\n > y = base.dists.poisson.mgf( 0.0, NaN )\n NaN\n > y = base.dists.poisson.mgf( -2.0, -1.0 )\n NaN\n\n\nbase.dists.poisson.mgf.factory( λ )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Poisson distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.poisson.mgf.factory( 2.0 );\n > var y = myMGF( 0.1 )\n ~1.234\n\n","base.dists.poisson.mgf.factory":"\nbase.dists.poisson.mgf.factory( λ )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Poisson distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.poisson.mgf.factory( 2.0 );\n > var y = myMGF( 0.1 )\n ~1.234","base.dists.poisson.mode":"\nbase.dists.poisson.mode( λ )\n Returns the mode of a Poisson distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: integer\n Mode.\n\n Examples\n --------\n > var v = base.dists.poisson.mode( 11.0 )\n 11\n > v = base.dists.poisson.mode( 4.5 )\n 4\n\n","base.dists.poisson.pmf":"\nbase.dists.poisson.pmf( x, λ )\n Evaluates the probability mass function (PMF) for a Poisson\n distribution with mean parameter `λ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Evaluated PMF.\n\n Examples\n --------\n > var y = base.dists.poisson.pmf( 4.0, 3.0 )\n ~0.168\n > y = base.dists.poisson.pmf( 1.0, 3.0 )\n ~0.149\n > y = base.dists.poisson.pmf( -1.0, 2.0 )\n 0.0\n > y = base.dists.poisson.pmf( 0.0, NaN )\n NaN\n > y = base.dists.poisson.pmf( NaN, 0.5 )\n NaN\n\n // Negative mean parameter:\n > y = base.dists.poisson.pmf( 2.0, -0.5 )\n NaN\n\n // Degenerate distribution at `λ = 0`:\n > y = base.dists.poisson.pmf( 2.0, 0.0 )\n 0.0\n > y = base.dists.poisson.pmf( 0.0, 0.0 )\n 1.0\n\n\nbase.dists.poisson.pmf.factory( λ )\n Returns a function for evaluating the probability mass function (PMF)\n of a Poisson distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var mypmf = base.dists.poisson.pmf.factory( 1.0 );\n > var y = mypmf( 3.0 )\n ~0.061\n > y = mypmf( 1.0 )\n ~0.368\n\n","base.dists.poisson.pmf.factory":"\nbase.dists.poisson.pmf.factory( λ )\n Returns a function for evaluating the probability mass function (PMF)\n of a Poisson distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n pmf: Function\n Probability mass function (PMF).\n\n Examples\n --------\n > var mypmf = base.dists.poisson.pmf.factory( 1.0 );\n > var y = mypmf( 3.0 )\n ~0.061\n > y = mypmf( 1.0 )\n ~0.368","base.dists.poisson.Poisson":"\nbase.dists.poisson.Poisson( [λ] )\n Returns a Poisson distribution object.\n\n Parameters\n ----------\n λ: number (optional)\n Mean parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n poisson: Object\n Distribution instance.\n\n poisson.lambda: number\n Mean parameter. If set, the value must be greater than `0`.\n\n poisson.entropy: number\n Read-only property which returns the differential entropy.\n\n poisson.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n poisson.mean: number\n Read-only property which returns the expected value.\n\n poisson.median: number\n Read-only property which returns the median.\n\n poisson.mode: number\n Read-only property which returns the mode.\n\n poisson.skewness: number\n Read-only property which returns the skewness.\n\n poisson.stdev: number\n Read-only property which returns the standard deviation.\n\n poisson.variance: number\n Read-only property which returns the variance.\n\n poisson.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n poisson.logpmf: Function\n Evaluates the natural logarithm of the probability mass function (PMF).\n\n poisson.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n poisson.pmf: Function\n Evaluates the probability mass function (PMF).\n\n poisson.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var poisson = base.dists.poisson.Poisson( 6.0 );\n > poisson.lambda\n 6.0\n > poisson.entropy\n ~2.3\n > poisson.kurtosis\n ~0.167\n > poisson.mean\n 6.0\n > poisson.median\n 6.0\n > poisson.mode\n 6.0\n > poisson.skewness\n ~0.408\n > poisson.stdev\n ~2.449\n > poisson.variance\n 6.0\n > poisson.cdf( 4.0 )\n ~0.285\n > poisson.logpmf( 2.0 )\n ~-3.11\n > poisson.mgf( 0.5 )\n ~49.025\n > poisson.pmf( 2.0 )\n ~0.045\n > poisson.quantile( 0.5 )\n 6.0\n\n","base.dists.poisson.quantile":"\nbase.dists.poisson.quantile( p, λ )\n Evaluates the quantile function for a Poisson distribution with mean\n parameter `λ` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.poisson.quantile( 0.5, 2.0 )\n 2\n > y = base.dists.poisson.quantile( 0.9, 4.0 )\n 7\n > y = base.dists.poisson.quantile( 0.1, 200.0 )\n 182\n\n > y = base.dists.poisson.quantile( 1.1, 0.0 )\n NaN\n > y = base.dists.poisson.quantile( -0.2, 0.0 )\n NaN\n\n > y = base.dists.poisson.quantile( NaN, 0.5 )\n NaN\n > y = base.dists.poisson.quantile( 0.0, NaN )\n NaN\n\n // Negative mean parameter:\n > y = base.dists.poisson.quantile( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution at `λ = 0`:\n > y = base.dists.poisson.quantile( 0.1, 0.0 )\n 0.0\n > y = base.dists.poisson.quantile( 0.9, 0.0 )\n 0.0\n\n\nbase.dists.poisson.quantile.factory( λ )\n Returns a function for evaluating the quantile function of a Poisson\n distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.poisson.quantile.factory( 0.4 );\n > var y = myQuantile( 0.4 )\n 0.0\n > y = myQuantile( 1.0 )\n Infinity\n\n","base.dists.poisson.quantile.factory":"\nbase.dists.poisson.quantile.factory( λ )\n Returns a function for evaluating the quantile function of a Poisson\n distribution with mean parameter `λ`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.poisson.quantile.factory( 0.4 );\n > var y = myQuantile( 0.4 )\n 0.0\n > y = myQuantile( 1.0 )\n Infinity","base.dists.poisson.skewness":"\nbase.dists.poisson.skewness( λ )\n Returns the skewness of a Poisson distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a nonpositive value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.poisson.skewness( 11.0 )\n ~0.302\n > v = base.dists.poisson.skewness( 4.5 )\n ~0.471\n\n","base.dists.poisson.stdev":"\nbase.dists.poisson.stdev( λ )\n Returns the standard deviation of a Poisson distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.poisson.stdev( 11.0 )\n ~3.317\n > v = base.dists.poisson.stdev( 4.5 )\n ~2.121\n\n","base.dists.poisson.variance":"\nbase.dists.poisson.variance( λ )\n Returns the variance of a Poisson distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `λ`, the function returns `NaN`.\n\n Parameters\n ----------\n λ: number\n Mean parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.poisson.variance( 11.0 )\n 11.0\n > v = base.dists.poisson.variance( 4.5 )\n 4.5\n\n","base.dists.rayleigh.cdf":"\nbase.dists.rayleigh.cdf( x, sigma )\n Evaluates the cumulative distribution function (CDF) for a Rayleigh\n distribution with scale parameter `sigma` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `sigma`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n sigma: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.rayleigh.cdf( 2.0, 3.0 )\n ~0.199\n > y = base.dists.rayleigh.cdf( 1.0, 2.0 )\n ~0.118\n > y = base.dists.rayleigh.cdf( -1.0, 4.0 )\n 0.0\n > y = base.dists.rayleigh.cdf( NaN, 1.0 )\n NaN\n > y = base.dists.rayleigh.cdf( 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.rayleigh.cdf( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution when `sigma = 0.0`:\n > y = base.dists.rayleigh.cdf( -2.0, 0.0 )\n 0.0\n > y = base.dists.rayleigh.cdf( 0.0, 0.0 )\n 1.0\n > y = base.dists.rayleigh.cdf( 2.0, 0.0 )\n 1.0\n\n\nbase.dists.rayleigh.cdf.factory( sigma )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Rayleigh distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.rayleigh.cdf.factory( 0.5 );\n > var y = myCDF( 1.0 )\n ~0.865\n > y = myCDF( 0.5 )\n ~0.393\n\n","base.dists.rayleigh.cdf.factory":"\nbase.dists.rayleigh.cdf.factory( sigma )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Rayleigh distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.rayleigh.cdf.factory( 0.5 );\n > var y = myCDF( 1.0 )\n ~0.865\n > y = myCDF( 0.5 )\n ~0.393","base.dists.rayleigh.entropy":"\nbase.dists.rayleigh.entropy( σ )\n Returns the differential entropy of a Rayleigh distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.rayleigh.entropy( 11.0 )\n ~3.34\n > v = base.dists.rayleigh.entropy( 4.5 )\n ~2.446\n\n","base.dists.rayleigh.kurtosis":"\nbase.dists.rayleigh.kurtosis( σ )\n Returns the excess kurtosis of a Rayleigh distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.rayleigh.kurtosis( 11.0 )\n ~0.245\n > v = base.dists.rayleigh.kurtosis( 4.5 )\n ~0.245\n\n","base.dists.rayleigh.logcdf":"\nbase.dists.rayleigh.logcdf( x, sigma )\n Evaluates the logarithm of the cumulative distribution function (CDF) for a\n Rayleigh distribution with scale parameter `sigma` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `sigma`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n sigma: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.rayleigh.logcdf( 2.0, 3.0 )\n ~-1.613\n > y = base.dists.rayleigh.logcdf( 1.0, 2.0 )\n ~-2.141\n > y = base.dists.rayleigh.logcdf( -1.0, 4.0 )\n -Infinity\n > y = base.dists.rayleigh.logcdf( NaN, 1.0 )\n NaN\n > y = base.dists.rayleigh.logcdf( 0.0, NaN )\n NaN\n // Negative scale parameter:\n > y = base.dists.rayleigh.logcdf( 2.0, -1.0 )\n NaN\n\n\nbase.dists.rayleigh.logcdf.factory( sigma )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Rayleigh distribution with scale parameter\n `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.rayleigh.logcdf.factory( 0.5 );\n > var y = mylogcdf( 1.0 )\n ~-0.145\n > y = mylogcdf( 0.5 )\n ~-0.933\n\n","base.dists.rayleigh.logcdf.factory":"\nbase.dists.rayleigh.logcdf.factory( sigma )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Rayleigh distribution with scale parameter\n `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.rayleigh.logcdf.factory( 0.5 );\n > var y = mylogcdf( 1.0 )\n ~-0.145\n > y = mylogcdf( 0.5 )\n ~-0.933","base.dists.rayleigh.logpdf":"\nbase.dists.rayleigh.logpdf( x, sigma )\n Evaluates the logarithm of the probability density function (PDF) for a\n Rayleigh distribution with scale parameter `sigma` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `sigma`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n sigma: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.rayleigh.logpdf( 0.3, 1.0 )\n ~-1.249\n > y = base.dists.rayleigh.logpdf( 2.0, 0.8 )\n ~-1.986\n > y = base.dists.rayleigh.logpdf( -1.0, 0.5 )\n -Infinity\n > y = base.dists.rayleigh.logpdf( 0.0, NaN )\n NaN\n > y = base.dists.rayleigh.logpdf( NaN, 2.0 )\n NaN\n // Negative scale parameter:\n > y = base.dists.rayleigh.logpdf( 2.0, -1.0 )\n NaN\n\n\nbase.dists.rayleigh.logpdf.factory( sigma )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Rayleigh distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.rayleigh.logpdf.factory( 4.0 );\n > var y = mylogpdf( 6.0 )\n ~-2.106\n > y = mylogpdf( 4.0 )\n ~-1.886\n\n","base.dists.rayleigh.logpdf.factory":"\nbase.dists.rayleigh.logpdf.factory( sigma )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Rayleigh distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.rayleigh.logpdf.factory( 4.0 );\n > var y = mylogpdf( 6.0 )\n ~-2.106\n > y = mylogpdf( 4.0 )\n ~-1.886","base.dists.rayleigh.mean":"\nbase.dists.rayleigh.mean( σ )\n Returns the expected value of a Rayleigh distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ <= 0`, the function returns `NaN`.\n\n Parameters\n ----------\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.rayleigh.mean( 11.0 )\n ~13.786\n > v = base.dists.rayleigh.mean( 4.5 )\n ~5.64\n\n","base.dists.rayleigh.median":"\nbase.dists.rayleigh.median( σ )\n Returns the median of a Rayleigh distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.rayleigh.median( 11.0 )\n ~12.952\n > v = base.dists.rayleigh.median( 4.5 )\n ~5.298\n\n","base.dists.rayleigh.mgf":"\nbase.dists.rayleigh.mgf( t, sigma )\n Evaluates the moment-generating function (MGF) for a Rayleigh distribution\n with scale parameter `sigma` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `sigma`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n sigma: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.rayleigh.mgf( 1.0, 3.0 )\n ~678.508\n > y = base.dists.rayleigh.mgf( 1.0, 2.0 )\n ~38.65\n > y = base.dists.rayleigh.mgf( -1.0, 4.0 )\n ~-0.947\n > y = base.dists.rayleigh.mgf( NaN, 1.0 )\n NaN\n > y = base.dists.rayleigh.mgf( 0.0, NaN )\n NaN\n > y = base.dists.rayleigh.mgf( 0.5, -1.0 )\n NaN\n\n\nbase.dists.rayleigh.mgf.factory( sigma )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Rayleigh distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.rayleigh.mgf.factory( 0.5 );\n > var y = myMGF( 1.0 )\n ~2.715\n > y = myMGF( 0.5 )\n ~1.888\n\n","base.dists.rayleigh.mgf.factory":"\nbase.dists.rayleigh.mgf.factory( sigma )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Rayleigh distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.rayleigh.mgf.factory( 0.5 );\n > var y = myMGF( 1.0 )\n ~2.715\n > y = myMGF( 0.5 )\n ~1.888","base.dists.rayleigh.mode":"\nbase.dists.rayleigh.mode( σ )\n Returns the mode of a Rayleigh distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.rayleigh.mode( 11.0 )\n 11.0\n > v = base.dists.rayleigh.mode( 4.5 )\n 4.5\n\n","base.dists.rayleigh.pdf":"\nbase.dists.rayleigh.pdf( x, sigma )\n Evaluates the probability density function (PDF) for a Rayleigh\n distribution with scale parameter `sigma` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `sigma`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n sigma: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.rayleigh.pdf( 0.3, 1.0 )\n ~0.287\n > y = base.dists.rayleigh.pdf( 2.0, 0.8 )\n ~0.137\n > y = base.dists.rayleigh.pdf( -1.0, 0.5 )\n 0.0\n > y = base.dists.rayleigh.pdf( 0.0, NaN )\n NaN\n > y = base.dists.rayleigh.pdf( NaN, 2.0 )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.rayleigh.pdf( 2.0, -1.0 )\n NaN\n\n // Degenerate distribution when `sigma = 0.0`:\n > y = base.dists.rayleigh.pdf( -2.0, 0.0 )\n 0.0\n > y = base.dists.rayleigh.pdf( 0.0, 0.0 )\n Infinity\n > y = base.dists.rayleigh.pdf( 2.0, 0.0 )\n 0.0\n\n\nbase.dists.rayleigh.pdf.factory( sigma )\n Returns a function for evaluating the probability density function (PDF) of\n a Rayleigh distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.rayleigh.pdf.factory( 4.0 );\n > var y = myPDF( 6.0 )\n ~0.122\n > y = myPDF( 4.0 )\n ~0.152\n\n","base.dists.rayleigh.pdf.factory":"\nbase.dists.rayleigh.pdf.factory( sigma )\n Returns a function for evaluating the probability density function (PDF) of\n a Rayleigh distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.rayleigh.pdf.factory( 4.0 );\n > var y = myPDF( 6.0 )\n ~0.122\n > y = myPDF( 4.0 )\n ~0.152","base.dists.rayleigh.quantile":"\nbase.dists.rayleigh.quantile( p, sigma )\n Evaluates the quantile function for a Rayleigh distribution with scale\n parameter `sigma` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative probability for `sigma`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n sigma: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.rayleigh.quantile( 0.8, 1.0 )\n ~1.794\n > y = base.dists.rayleigh.quantile( 0.5, 4.0 )\n ~4.71\n\n > y = base.dists.rayleigh.quantile( 1.1, 1.0 )\n NaN\n > y = base.dists.rayleigh.quantile( -0.2, 1.0 )\n NaN\n\n > y = base.dists.rayleigh.quantile( NaN, 1.0 )\n NaN\n > y = base.dists.rayleigh.quantile( 0.0, NaN )\n NaN\n\n // Negative scale parameter:\n > y = base.dists.rayleigh.quantile( 0.5, -1.0 )\n NaN\n\n\nbase.dists.rayleigh.quantile.factory( sigma )\n Returns a function for evaluating the quantile function of a Rayleigh\n distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.rayleigh.quantile.factory( 0.4 );\n > var y = myQuantile( 0.4 )\n ~0.404\n > y = myQuantile( 1.0 )\n Infinity\n\n","base.dists.rayleigh.quantile.factory":"\nbase.dists.rayleigh.quantile.factory( sigma )\n Returns a function for evaluating the quantile function of a Rayleigh\n distribution with scale parameter `sigma`.\n\n Parameters\n ----------\n sigma: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.rayleigh.quantile.factory( 0.4 );\n > var y = myQuantile( 0.4 )\n ~0.404\n > y = myQuantile( 1.0 )\n Infinity","base.dists.rayleigh.Rayleigh":"\nbase.dists.rayleigh.Rayleigh( [σ] )\n Returns a Rayleigh distribution object.\n\n Parameters\n ----------\n σ: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n rayleigh: Object\n Distribution instance.\n\n rayleigh.sigma: number\n Scale parameter. If set, the value must be greater than `0`.\n\n rayleigh.entropy: number\n Read-only property which returns the differential entropy.\n\n rayleigh.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n rayleigh.mean: number\n Read-only property which returns the expected value.\n\n rayleigh.median: number\n Read-only property which returns the median.\n\n rayleigh.mode: number\n Read-only property which returns the mode.\n\n rayleigh.skewness: number\n Read-only property which returns the skewness.\n\n rayleigh.stdev: number\n Read-only property which returns the standard deviation.\n\n rayleigh.variance: number\n Read-only property which returns the variance.\n\n rayleigh.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n rayleigh.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n rayleigh.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n rayleigh.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n rayleigh.pdf: Function\n Evaluates the probability density function (PDF).\n\n rayleigh.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var rayleigh = base.dists.rayleigh.Rayleigh( 6.0 );\n > rayleigh.sigma\n 6.0\n > rayleigh.entropy\n ~2.734\n > rayleigh.kurtosis\n ~0.245\n > rayleigh.mean\n ~7.52\n > rayleigh.median\n ~7.064\n > rayleigh.mode\n 6.0\n > rayleigh.skewness\n ~0.631\n > rayleigh.stdev\n ~3.931\n > rayleigh.variance\n ~15.451\n > rayleigh.cdf( 1.0 )\n ~0.014\n > rayleigh.logcdf( 1.0 )\n ~-4.284\n > rayleigh.logpdf( 1.5 )\n ~-3.209\n > rayleigh.mgf( -0.5 )\n ~-0.91\n > rayleigh.pdf( 1.5 )\n ~0.04\n > rayleigh.quantile( 0.5 )\n ~7.064\n\n","base.dists.rayleigh.skewness":"\nbase.dists.rayleigh.skewness( σ )\n Returns the skewness of a Rayleigh distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.rayleigh.skewness( 11.0 )\n ~0.631\n > v = base.dists.rayleigh.skewness( 4.5 )\n ~0.631\n\n","base.dists.rayleigh.stdev":"\nbase.dists.rayleigh.stdev( σ )\n Returns the standard deviation of a Rayleigh distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.rayleigh.stdev( 9.0 )\n ~5.896\n > v = base.dists.rayleigh.stdev( 4.5 )\n ~2.948\n\n","base.dists.rayleigh.variance":"\nbase.dists.rayleigh.variance( σ )\n Returns the variance of a Rayleigh distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `σ < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n σ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.rayleigh.variance( 9.0 )\n ~34.765\n > v = base.dists.rayleigh.variance( 4.5 )\n ~8.691\n\n","base.dists.signrank.cdf":"\nbase.dists.signrank.cdf( x, n )\n Evaluates the cumulative distribution function (CDF) for the distribution of\n the Wilcoxon signed rank test statistic with `n` observations.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `x`, the function returns `NaN`.\n\n If not provided a positive integer for `n`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Number of observations.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.signrank.cdf( 3, 7 )\n ~0.039\n > y = base.dists.signrank.cdf( 1.8, 3 )\n ~0.375\n > y = base.dists.signrank.cdf( -1.0, 40 )\n 0.0\n > y = base.dists.signrank.cdf( NaN, 10 )\n NaN\n > y = base.dists.signrank.cdf( 0.0, NaN )\n NaN\n\n\nbase.dists.signrank.cdf.factory( n )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of the distribution of the Wilcoxon signed rank test statistic.\n\n Parameters\n ----------\n n: integer\n Number of observations.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.signrank.cdf.factory( 8 );\n > var y = myCDF( 5.7 )\n ~0.055\n > y = myCDF( 2.2 )\n ~0.012\n\n","base.dists.signrank.cdf.factory":"\nbase.dists.signrank.cdf.factory( n )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of the distribution of the Wilcoxon signed rank test statistic.\n\n Parameters\n ----------\n n: integer\n Number of observations.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.signrank.cdf.factory( 8 );\n > var y = myCDF( 5.7 )\n ~0.055\n > y = myCDF( 2.2 )\n ~0.012","base.dists.signrank.pdf":"\nbase.dists.signrank.pdf( x, n )\n Evaluates the probability density function (PDF) for the distribution of\n the Wilcoxon signed rank test statistic with `n` observations.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a negative value for `x`, the function returns `NaN`.\n\n If not provided a positive integer for `n`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Number of observations.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.signrank.pdf( 3, 7 )\n ~0.0156\n > y = base.dists.signrank.pdf( 1.8, 3 )\n 0.0\n > y = base.dists.signrank.pdf( -1.0, 40 )\n 0.0\n > y = base.dists.signrank.pdf( NaN, 10 )\n NaN\n > y = base.dists.signrank.pdf( 0.0, NaN )\n NaN\n\n\nbase.dists.signrank.pdf.factory( n )\n Returns a function for evaluating the probability density function (PDF)\n of the distribution of the Wilcoxon signed rank test statistic.\n\n Parameters\n ----------\n n: integer\n Number of observations.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.signrank.pdf.factory( 8 );\n > var y = myPDF( 6.0 )\n ~0.0156\n > y = myPDF( 2.0 )\n ~0.0039\n\n","base.dists.signrank.pdf.factory":"\nbase.dists.signrank.pdf.factory( n )\n Returns a function for evaluating the probability density function (PDF)\n of the distribution of the Wilcoxon signed rank test statistic.\n\n Parameters\n ----------\n n: integer\n Number of observations.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.signrank.pdf.factory( 8 );\n > var y = myPDF( 6.0 )\n ~0.0156\n > y = myPDF( 2.0 )\n ~0.0039","base.dists.signrank.quantile":"\nbase.dists.signrank.quantile( p, n )\n Evaluates the quantile function for the Wilcoxon signed rank test statistic\n with `n` observations at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If not provided a positive integer for `n`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n n: integer\n Number of observations.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.signrank.quantile( 0.8, 5 )\n 11\n > y = base.dists.signrank.quantile( 0.5, 4 )\n 5\n\n > y = base.dists.signrank.quantile( 1.1, 5 )\n NaN\n > y = base.dists.signrank.quantile( -0.2, 5 )\n NaN\n\n > y = base.dists.signrank.quantile( NaN, 5 )\n NaN\n > y = base.dists.signrank.quantile( 0.0, NaN )\n NaN\n\n\nbase.dists.signrank.quantile.factory( n )\n Returns a function for evaluating the quantile function of the Wilcoxon\n signed rank test statistic with `n` observations.\n\n Parameters\n ----------\n n: integer\n Number of observations.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.signrank.quantile.factory( 8 );\n > var y = myQuantile( 0.4 )\n 16\n > y = myQuantile( 1.0 )\n 36\n\n","base.dists.signrank.quantile.factory":"\nbase.dists.signrank.quantile.factory( n )\n Returns a function for evaluating the quantile function of the Wilcoxon\n signed rank test statistic with `n` observations.\n\n Parameters\n ----------\n n: integer\n Number of observations.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.signrank.quantile.factory( 8 );\n > var y = myQuantile( 0.4 )\n 16\n > y = myQuantile( 1.0 )\n 36","base.dists.studentizedRange.cdf":"\nbase.dists.studentizedRange.cdf( x, r, v[, nranges] )\n Evaluates the cumulative distribution function (CDF) of a studentized range\n distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `r < 2` or `v < 2`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n r: number\n Sample size for range (same for each group).\n\n v: number\n Degrees of freedom.\n\n nranges: integer\n Number of groups whose maximum range is considered. Default: 1.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.studentizedRange.cdf( 0.5, 3.0, 2.0 )\n ~0.0644\n\n > y = base.dists.studentizedRange.cdf( 12.1, 17.0, 2.0 )\n ~0.913\n\n\nbase.dists.studentizedRange.cdf.factory( r, v[, nranges] )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a studentized range distribution.\n\n Parameters\n ----------\n r: number\n Number of samples.\n\n v: number\n Degrees of freedom.\n\n nranges: integer\n Number of groups whose maximum range is considered. Default: 1.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.studentizedRange.cdf.factory( 3.0, 2.0 );\n > var y = mycdf( 3.0 )\n ~0.712\n > y = mycdf( 1.0 )\n ~0.216\n\n","base.dists.studentizedRange.cdf.factory":"\nbase.dists.studentizedRange.cdf.factory( r, v[, nranges] )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a studentized range distribution.\n\n Parameters\n ----------\n r: number\n Number of samples.\n\n v: number\n Degrees of freedom.\n\n nranges: integer\n Number of groups whose maximum range is considered. Default: 1.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.studentizedRange.cdf.factory( 3.0, 2.0 );\n > var y = mycdf( 3.0 )\n ~0.712\n > y = mycdf( 1.0 )\n ~0.216","base.dists.studentizedRange.quantile":"\nbase.dists.studentizedRange.quantile( p, r, v[, nranges] )\n Evaluates the quantile function for a studentized range distribution.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `r < 2` or `v < 2`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n r: number\n Sample size for range (same for each group).\n\n v: number\n Degrees of freedom.\n\n nranges: integer\n Number of groups whose maximum range is considered. Default: 1.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = quantile( 0.5, 3.0, 2.0 )\n ~0.0644\n\n > y = quantile( 0.9, 17.0, 2.0 )\n ~0.913\n\n > y = quantile( 0.5, 3.0, 2.0, 2 )\n ~0.01\n\n > y = base.dists.studentizedRange.quantile( -0.2, 3.0, 3.0 )\n NaN\n\n > y = base.dists.studentizedRange.quantile( NaN, 2.0, 2.0 )\n NaN\n > y = base.dists.studentizedRange.quantile( 0.0, NaN, 2.0 )\n NaN\n\n > y = base.dists.studentizedRange.quantile( 0.5, -1.0, 2.0 )\n NaN\n\n\nbase.dists.studentizedRange.quantile.factory( r, v[, nranges] )\n Returns a function for evaluating the quantile function of a studentized\n range distribution.\n\n Parameters\n ----------\n r: number\n Sample size for range (same for each group).\n\n v: number\n Degrees of freedom.\n\n nranges: integer\n Number of groups whose maximum range is considered. Default: 1.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = quantile.factory( 3.0, 3.0 );\n > var y = myQuantile( 0.5 )\n ~1.791\n\n > y = myQuantile( 0.8 )\n ~3.245\n\n","base.dists.studentizedRange.quantile.factory":"\nbase.dists.studentizedRange.quantile.factory( r, v[, nranges] )\n Returns a function for evaluating the quantile function of a studentized\n range distribution.\n\n Parameters\n ----------\n r: number\n Sample size for range (same for each group).\n\n v: number\n Degrees of freedom.\n\n nranges: integer\n Number of groups whose maximum range is considered. Default: 1.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = quantile.factory( 3.0, 3.0 );\n > var y = myQuantile( 0.5 )\n ~1.791\n\n > y = myQuantile( 0.8 )\n ~3.245","base.dists.t.cdf":"\nbase.dists.t.cdf( x, v )\n Evaluates the cumulative distribution function (CDF) for a Student's t\n distribution with degrees of freedom `v` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a non-positive value for `v`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.t.cdf( 2.0, 0.1 )\n ~0.611\n > y = base.dists.t.cdf( 1.0, 2.0 )\n ~0.789\n > y = base.dists.t.cdf( -1.0, 4.0 )\n ~0.187\n > y = base.dists.t.cdf( NaN, 1.0 )\n NaN\n > y = base.dists.t.cdf( 0.0, NaN )\n NaN\n > y = base.dists.t.cdf( 2.0, -1.0 )\n NaN\n\n\nbase.dists.t.cdf.factory( v )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Student's t distribution with degrees of freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.t.cdf.factory( 0.5 );\n > var y = mycdf( 3.0 )\n ~0.816\n > y = mycdf( 1.0 )\n ~0.699\n\n","base.dists.t.cdf.factory":"\nbase.dists.t.cdf.factory( v )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Student's t distribution with degrees of freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.t.cdf.factory( 0.5 );\n > var y = mycdf( 3.0 )\n ~0.816\n > y = mycdf( 1.0 )\n ~0.699","base.dists.t.entropy":"\nbase.dists.t.entropy( v )\n Returns the differential entropy of a Student's t distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `v < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.t.entropy( 11.0 )\n ~1.512\n > v = base.dists.t.entropy( 4.5 )\n ~1.652\n\n","base.dists.t.kurtosis":"\nbase.dists.t.kurtosis( v )\n Returns the excess kurtosis of a Student's t distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `v <= 2`, the function returns `NaN`.\n\n If provided `2 < v <= 4`, the function returns positive infinity.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.t.kurtosis( 11.0 )\n ~0.857\n > v = base.dists.t.kurtosis( 4.5 )\n 12.0\n\n","base.dists.t.logcdf":"\nbase.dists.t.logcdf( x, v )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a Student's t distribution with degrees of freedom `v` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a non-positive value for `v`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.t.logcdf( 2.0, 0.1 )\n ~-0.493\n > y = base.dists.t.logcdf( 1.0, 2.0 )\n ~-0.237\n > y = base.dists.t.logcdf( -1.0, 4.0 )\n ~-1.677\n > y = base.dists.t.logcdf( NaN, 1.0 )\n NaN\n > y = base.dists.t.logcdf( 0.0, NaN )\n NaN\n > y = base.dists.t.logcdf( 2.0, -1.0 )\n NaN\n\n\nbase.dists.t.logcdf.factory( v )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a Student's t distribution with degrees of\n freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.t.logcdf.factory( 0.5 );\n > var y = mylogcdf( 3.0 )\n ~-0.203\n > y = mylogcdf( 1.0 )\n ~-0.358\n\n","base.dists.t.logcdf.factory":"\nbase.dists.t.logcdf.factory( v )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a Student's t distribution with degrees of\n freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.t.logcdf.factory( 0.5 );\n > var y = mylogcdf( 3.0 )\n ~-0.203\n > y = mylogcdf( 1.0 )\n ~-0.358","base.dists.t.logpdf":"\nbase.dists.t.logpdf( x, v )\n Evaluates the natural logarithm of the probability density function (PDF)\n for a Student's t distribution with degrees of freedom `v` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a non-positive value for `v`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.t.logpdf( 0.3, 4.0 )\n ~-1.036\n > y = base.dists.t.logpdf( 2.0, 0.7 )\n ~-2.841\n > y = base.dists.t.logpdf( -1.0, 0.5 )\n ~-2.134\n > y = base.dists.t.logpdf( 0.0, NaN )\n NaN\n > y = base.dists.t.logpdf( NaN, 2.0 )\n NaN\n > y = base.dists.t.logpdf( 2.0, -1.0 )\n NaN\n\n\nbase.dists.t.logpdf.factory( v )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a Student's t distribution with degrees of\n freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.t.logpdf.factory( 3.0 );\n > var y = mylogPDF( 1.0 )\n ~-1.576\n\n","base.dists.t.logpdf.factory":"\nbase.dists.t.logpdf.factory( v )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a Student's t distribution with degrees of\n freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.t.logpdf.factory( 3.0 );\n > var y = mylogPDF( 1.0 )\n ~-1.576","base.dists.t.mean":"\nbase.dists.t.mean( v )\n Returns the expected value of a Student's t distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `v <= 1`, the function returns `NaN`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.t.mean( 11.0 )\n 0.0\n > v = base.dists.t.mean( 4.5 )\n 0.0\n\n","base.dists.t.median":"\nbase.dists.t.median( v )\n Returns the median of a Student's t distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `v < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.t.median( 11.0 )\n 0.0\n > v = base.dists.t.median( 4.5 )\n 0.0\n\n","base.dists.t.mode":"\nbase.dists.t.mode( v )\n Returns the mode of a Student's t distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `v < 0`, the function returns `NaN`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.t.mode( 11.0 )\n 0.0\n > v = base.dists.t.mode( 4.5 )\n 0.0\n\n","base.dists.t.pdf":"\nbase.dists.t.pdf( x, v )\n Evaluates the probability density function (PDF) for a Student's t\n distribution with degrees of freedom `v` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a non-positive value for `v`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.t.pdf( 0.3, 4.0 )\n ~0.355\n > y = base.dists.t.pdf( 2.0, 0.7 )\n ~0.058\n > y = base.dists.t.pdf( -1.0, 0.5 )\n ~0.118\n > y = base.dists.t.pdf( 0.0, NaN )\n NaN\n > y = base.dists.t.pdf( NaN, 2.0 )\n NaN\n > y = base.dists.t.pdf( 2.0, -1.0 )\n NaN\n\n\nbase.dists.t.pdf.factory( v )\n Returns a function for evaluating the probability density function (PDF)\n of a Student's t distribution with degrees of freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.t.pdf.factory( 3.0 );\n > var y = myPDF( 1.0 )\n ~0.207\n\n","base.dists.t.pdf.factory":"\nbase.dists.t.pdf.factory( v )\n Returns a function for evaluating the probability density function (PDF)\n of a Student's t distribution with degrees of freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.t.pdf.factory( 3.0 );\n > var y = myPDF( 1.0 )\n ~0.207","base.dists.t.quantile":"\nbase.dists.t.quantile( p, v )\n Evaluates the quantile function for a Student's t distribution with degrees\n of freedom `v` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a non-positive value for `v`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.t.quantile( 0.8, 1.0 )\n ~1.376\n > y = base.dists.t.quantile( 0.1, 1.0 )\n ~-3.078\n > y = base.dists.t.quantile( 0.5, 0.1 )\n 0.0\n\n > y = base.dists.t.quantile( -0.2, 0.1 )\n NaN\n\n > y = base.dists.t.quantile( NaN, 1.0 )\n NaN\n > y = base.dists.t.quantile( 0.0, NaN )\n NaN\n\n > y = base.dists.t.quantile( 0.5, -1.0 )\n NaN\n\n\nbase.dists.t.quantile.factory( v )\n Returns a function for evaluating the quantile function of a Student's t\n distribution with degrees of freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.t.quantile.factory( 4.0 );\n > var y = myQuantile( 0.2 )\n ~-0.941\n > y = myQuantile( 0.9 )\n ~1.533\n\n","base.dists.t.quantile.factory":"\nbase.dists.t.quantile.factory( v )\n Returns a function for evaluating the quantile function of a Student's t\n distribution with degrees of freedom `v`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.t.quantile.factory( 4.0 );\n > var y = myQuantile( 0.2 )\n ~-0.941\n > y = myQuantile( 0.9 )\n ~1.533","base.dists.t.skewness":"\nbase.dists.t.skewness( v )\n Returns the skewness of a Student's t distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `v <= 3`, the function returns `NaN`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.t.skewness( 11.0 )\n 0.0\n > v = base.dists.t.skewness( 4.5 )\n 0.0\n\n","base.dists.t.stdev":"\nbase.dists.t.stdev( v )\n Returns the standard deviation of a Student's t distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `1 < v <= 2`, the function returns positive infinity.\n\n If provided `v <= 1`, the function returns `NaN`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.t.stdev( 9.0 )\n ~1.134\n > v = base.dists.t.stdev( 4.5 )\n ~1.342\n\n","base.dists.t.T":"\nbase.dists.t.T( [v] )\n Returns a Student's t distribution object.\n\n Parameters\n ----------\n v: number (optional)\n Degrees of freedom. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n t: Object\n Distribution instance.\n\n t.v: number\n Degrees of freedom. If set, the value must be greater than `0`.\n\n t.entropy: number\n Read-only property which returns the differential entropy.\n\n t.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n t.mean: number\n Read-only property which returns the expected value.\n\n t.median: number\n Read-only property which returns the median.\n\n t.mode: number\n Read-only property which returns the mode.\n\n t.skewness: number\n Read-only property which returns the skewness.\n\n t.stdev: number\n Read-only property which returns the standard deviation.\n\n t.variance: number\n Read-only property which returns the variance.\n\n t.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n t.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n t.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n t.pdf: Function\n Evaluates the probability density function (PDF).\n\n t.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var t = base.dists.t.T( 6.0 );\n > t.v\n 6.0\n > t.entropy\n ~1.592\n > t.kurtosis\n 3.0\n > t.mean\n 0.0\n > t.median\n 0.0\n > t.mode\n 0.0\n > t.skewness\n 0.0\n > t.stdev\n ~1.225\n > t.variance\n 1.5\n > t.cdf( 1.0 )\n ~0.822\n > t.logcdf( 1.0 )\n ~-0.196\n > t.logpdf( 1.5 )\n ~-2.075\n > t.pdf( 1.5 )\n ~0.126\n > t.quantile( 0.8 )\n ~0.906\n\n","base.dists.t.variance":"\nbase.dists.t.variance( v )\n Returns the variance of a Student's t distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `1 < v <= 2`, the function returns positive infinity.\n\n If provided `v <= 1`, the function returns `NaN`.\n\n Parameters\n ----------\n v: number\n Degrees of freedom.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.t.variance( 9.0 )\n ~1.286\n > v = base.dists.t.variance( 4.5 )\n ~1.8\n\n","base.dists.triangular.cdf":"\nbase.dists.triangular.cdf( x, a, b, c )\n Evaluates the cumulative distribution function (CDF) for a triangular\n distribution with minimum support `a`, maximum support `b`, and mode `c` at\n a value `x`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n If either `a`, `b`, or `c` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.triangular.cdf( 0.5, -1.0, 1.0, 0.0 )\n 0.875\n > y = base.dists.triangular.cdf( 0.5, -1.0, 1.0, 0.5 )\n 0.75\n > y = base.dists.triangular.cdf( -10.0, -20.0, 0.0, -2.0 )\n ~0.278\n > y = base.dists.triangular.cdf( -2.0, -1.0, 1.0, 0.0 )\n 0.0\n > y = base.dists.triangular.cdf( NaN, 0.0, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.cdf( 0.0, NaN, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.cdf( 0.0, 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.triangular.cdf( 2.0, 1.0, 0.0, NaN )\n NaN\n > y = base.dists.triangular.cdf( 2.0, 1.0, 0.0, 1.5 )\n NaN\n\n\nbase.dists.triangular.cdf.factory( a, b, c )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a triangular distribution with minimum support `a`, maximum support `b`,\n and mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.triangular.cdf.factory( 0.0, 10.0, 2.0 );\n > var y = mycdf( 0.5 )\n 0.0125\n > y = mycdf( 8.0 )\n 0.95\n\n\n","base.dists.triangular.cdf.factory":"\nbase.dists.triangular.cdf.factory( a, b, c )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a triangular distribution with minimum support `a`, maximum support `b`,\n and mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.triangular.cdf.factory( 0.0, 10.0, 2.0 );\n > var y = mycdf( 0.5 )\n 0.0125\n > y = mycdf( 8.0 )\n 0.95","base.dists.triangular.entropy":"\nbase.dists.triangular.entropy( a, b, c )\n Returns the differential entropy of a triangular distribution (in nats).\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.triangular.entropy( 0.0, 1.0, 0.8 )\n ~-0.193\n > v = base.dists.triangular.entropy( 4.0, 12.0, 5.0 )\n ~1.886\n > v = base.dists.triangular.entropy( 2.0, 8.0, 5.0 )\n ~1.599\n\n","base.dists.triangular.kurtosis":"\nbase.dists.triangular.kurtosis( a, b, c )\n Returns the excess kurtosis of a triangular distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.triangular.kurtosis( 0.0, 1.0, 0.8 )\n -0.6\n > v = base.dists.triangular.kurtosis( 4.0, 12.0, 5.0 )\n -0.6\n > v = base.dists.triangular.kurtosis( 2.0, 8.0, 5.0 )\n -0.6\n\n","base.dists.triangular.logcdf":"\nbase.dists.triangular.logcdf( x, a, b, c )\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF) for a triangular distribution with minimum support `a`, maximum\n support `b`, and mode `c` at a value `x`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n If either `a`, `b`, or `c` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.triangular.logcdf( 0.5, -1.0, 1.0, 0.0 )\n ~-0.134\n > y = base.dists.triangular.logcdf( 0.5, -1.0, 1.0, 0.5 )\n ~-0.288\n > y = base.dists.triangular.logcdf( -10.0, -20.0, 0.0, -2.0 )\n ~-1.281\n > y = base.dists.triangular.logcdf( -2.0, -1.0, 1.0, 0.0 )\n -Infinity\n > y = base.dists.triangular.logcdf( NaN, 0.0, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.logcdf( 0.0, NaN, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.logcdf( 0.0, 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.triangular.logcdf( 2.0, 1.0, 0.0, NaN )\n NaN\n > y = base.dists.triangular.logcdf( 2.0, 1.0, 0.0, 1.5 )\n NaN\n\n\nbase.dists.triangular.logcdf.factory( a, b, c )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a triangular distribution with minimum\n support `a`, maximum support `b`, and mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n cdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.triangular.logcdf.factory( 0.0, 10.0, 2.0 );\n > var y = mylogcdf( 0.5 )\n ~-4.382\n > y = mylogcdf( 8.0 )\n ~-0.051\n\n\n","base.dists.triangular.logcdf.factory":"\nbase.dists.triangular.logcdf.factory( a, b, c )\n Returns a function for evaluating the natural logarithm of the cumulative\n distribution function (CDF) of a triangular distribution with minimum\n support `a`, maximum support `b`, and mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n cdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.triangular.logcdf.factory( 0.0, 10.0, 2.0 );\n > var y = mylogcdf( 0.5 )\n ~-4.382\n > y = mylogcdf( 8.0 )\n ~-0.051","base.dists.triangular.logpdf":"\nbase.dists.triangular.logpdf( x, a, b, c )\n Evaluates the natural logarithm of the probability density function (PDF)\n for a triangular distribution with minimum support `a`, maximum support `b`,\n and mode `c` at a value `x`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n If either `a`, `b`, or `c` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.triangular.logpdf( 0.5, -1.0, 1.0, 0.0 )\n ~-0.693\n > y = base.dists.triangular.logpdf( 0.5, -1.0, 1.0, 0.5 )\n 0.0\n > y = base.dists.triangular.logpdf( -10.0, -20.0, 0.0, -2.0 )\n ~-2.89\n > y = base.dists.triangular.logpdf( -2.0, -1.0, 1.0, 0.0 )\n -Infinity\n > y = base.dists.triangular.logpdf( NaN, 0.0, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.logpdf( 0.0, NaN, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.logpdf( 0.0, 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.triangular.logpdf( 2.0, 1.0, 0.0, NaN )\n NaN\n > y = base.dists.triangular.logpdf( 2.0, 1.0, 0.0, 1.5 )\n NaN\n\n\nbase.dists.triangular.logpdf.factory( a, b, c )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a triangular distribution with minimum support\n `a`, maximum support `b`, and mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.triangular.logpdf.factory( 0.0, 10.0, 5.0 );\n > var y = mylogpdf( 2.0 )\n ~-2.526\n > y = mylogpdf( 12.0 )\n -Infinity\n\n\n","base.dists.triangular.logpdf.factory":"\nbase.dists.triangular.logpdf.factory( a, b, c )\n Returns a function for evaluating the natural logarithm of the probability\n density function (PDF) of a triangular distribution with minimum support\n `a`, maximum support `b`, and mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogpdf = base.dists.triangular.logpdf.factory( 0.0, 10.0, 5.0 );\n > var y = mylogpdf( 2.0 )\n ~-2.526\n > y = mylogpdf( 12.0 )\n -Infinity","base.dists.triangular.mean":"\nbase.dists.triangular.mean( a, b, c )\n Returns the expected value of a triangular distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.triangular.mean( 0.0, 1.0, 0.8 )\n ~0.6\n > v = base.dists.triangular.mean( 4.0, 12.0, 5.0 )\n 7.0\n > v = base.dists.triangular.mean( 2.0, 8.0, 5.0 )\n 5.0\n\n","base.dists.triangular.median":"\nbase.dists.triangular.median( a, b, c )\n Returns the median of a triangular distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.triangular.median( 0.0, 1.0, 0.8 )\n ~0.632\n > v = base.dists.triangular.median( 4.0, 12.0, 5.0 )\n ~6.708\n > v = base.dists.triangular.median( 2.0, 8.0, 5.0 )\n 5.0\n\n","base.dists.triangular.mgf":"\nbase.dists.triangular.mgf( t, a, b, c )\n Evaluates the moment-generating function (MGF) for a triangular distribution\n with minimum support `a`, maximum support `b`, and mode `c` at a value `t`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n If either `a`, `b`, or `c` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.triangular.mgf( 0.5, -1.0, 1.0, 0.0 )\n ~1.021\n > y = base.dists.triangular.mgf( 0.5, -1.0, 1.0, 0.5 )\n ~1.111\n > y = base.dists.triangular.mgf( -0.3, -20.0, 0.0, -2.0 )\n ~24.334\n > y = base.dists.triangular.mgf( -2.0, -1.0, 1.0, 0.0 )\n ~1.381\n > y = base.dists.triangular.mgf( NaN, 0.0, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.mgf( 0.0, NaN, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.mgf( 0.0, 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.triangular.mgf( 0.5, 1.0, 0.0, NaN )\n NaN\n > y = base.dists.triangular.mgf( 0.5, 1.0, 0.0, 1.5 )\n NaN\n\n\nbase.dists.triangular.mgf.factory( a, b, c )\n Returns a function for evaluating the moment-generating function (MGF) of a\n triangular distribution with minimum support `a`, maximum support `b`, and\n mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.triangular.mgf.factory( 0.0, 2.0, 1.0 );\n > var y = mymgf( -1.0 )\n ~0.3996\n > y = mymgf( 2.0 )\n ~10.205\n\n\n","base.dists.triangular.mgf.factory":"\nbase.dists.triangular.mgf.factory( a, b, c )\n Returns a function for evaluating the moment-generating function (MGF) of a\n triangular distribution with minimum support `a`, maximum support `b`, and\n mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.triangular.mgf.factory( 0.0, 2.0, 1.0 );\n > var y = mymgf( -1.0 )\n ~0.3996\n > y = mymgf( 2.0 )\n ~10.205","base.dists.triangular.mode":"\nbase.dists.triangular.mode( a, b, c )\n Returns the mode of a triangular distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.triangular.mode( 0.0, 1.0, 0.8 )\n 0.8\n > v = base.dists.triangular.mode( 4.0, 12.0, 5.0 )\n 5.0\n > v = base.dists.triangular.mode( 2.0, 8.0, 5.0 )\n 5.0\n\n","base.dists.triangular.pdf":"\nbase.dists.triangular.pdf( x, a, b, c )\n Evaluates the probability density function (PDF) for a triangular\n distribution with minimum support `a`, maximum support `b`, and mode `c` at\n a value `x`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n If either `a`, `b`, or `c` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.triangular.pdf( 0.5, -1.0, 1.0, 0.0 )\n 0.5\n > y = base.dists.triangular.pdf( 0.5, -1.0, 1.0, 0.5 )\n 1.0\n > y = base.dists.triangular.pdf( -10.0, -20.0, 0.0, -2.0 )\n ~0.056\n > y = base.dists.triangular.pdf( -2.0, -1.0, 1.0, 0.0 )\n 0.0\n > y = base.dists.triangular.pdf( NaN, 0.0, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.pdf( 0.0, NaN, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.pdf( 0.0, 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.triangular.pdf( 2.0, 1.0, 0.0, NaN )\n NaN\n > y = base.dists.triangular.pdf( 2.0, 1.0, 0.0, 1.5 )\n NaN\n\n\nbase.dists.triangular.pdf.factory( a, b, c )\n Returns a function for evaluating the probability density function (PDF) of\n a triangular distribution with minimum support `a`, maximum support `b`, and\n mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var mypdf = base.dists.triangular.pdf.factory( 0.0, 10.0, 5.0 );\n > var y = mypdf( 2.0 )\n 0.08\n > y = mypdf( 12.0 )\n 0.0\n\n\n","base.dists.triangular.pdf.factory":"\nbase.dists.triangular.pdf.factory( a, b, c )\n Returns a function for evaluating the probability density function (PDF) of\n a triangular distribution with minimum support `a`, maximum support `b`, and\n mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var mypdf = base.dists.triangular.pdf.factory( 0.0, 10.0, 5.0 );\n > var y = mypdf( 2.0 )\n 0.08\n > y = mypdf( 12.0 )\n 0.0","base.dists.triangular.quantile":"\nbase.dists.triangular.quantile( p, a, b, c )\n Evaluates the quantile function for a triangular distribution with minimum\n support `a`, maximum support `b`, and mode `c` at a value `x`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n If either `a`, `b`, or `c` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.triangular.quantile( 0.9, -1.0, 1.0, 0.0 )\n ~0.553\n > y = base.dists.triangular.quantile( 0.1, -1.0, 1.0, 0.5 )\n ~-0.452\n > y = base.dists.triangular.quantile( 0.1, -20.0, 0.0, -2.0 )\n -14.0\n > y = base.dists.triangular.quantile( 0.8, 0.0, 20.0, 0.0 )\n ~11.056\n\n > y = base.dists.triangular.quantile( 1.1, -1.0, 1.0, 0.0 )\n NaN\n > y = base.dists.triangular.quantile( -0.1, -1.0, 1.0, 0.0 )\n NaN\n\n > y = base.dists.triangular.quantile( NaN, 0.0, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.quantile( 0.3, NaN, 1.0, 0.5 )\n NaN\n > y = base.dists.triangular.quantile( 0.3, 0.0, NaN, 0.5 )\n NaN\n > y = base.dists.triangular.quantile( 0.3, 1.0, 0.0, NaN )\n NaN\n\n > y = base.dists.triangular.quantile( 0.3, 1.0, 0.0, 1.5 )\n NaN\n\n\nbase.dists.triangular.quantile.factory( a, b, c )\n Returns a function for evaluating the quantile function of a triangular\n distribution with minimum support `a`, maximum support `b`, and mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.triangular.quantile.factory( 2.0, 4.0, 2.5 );\n > var y = myquantile( 0.4 )\n ~2.658\n > y = myquantile( 0.8 )\n ~3.225\n\n\n","base.dists.triangular.quantile.factory":"\nbase.dists.triangular.quantile.factory( a, b, c )\n Returns a function for evaluating the quantile function of a triangular\n distribution with minimum support `a`, maximum support `b`, and mode `c`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myquantile = base.dists.triangular.quantile.factory( 2.0, 4.0, 2.5 );\n > var y = myquantile( 0.4 )\n ~2.658\n > y = myquantile( 0.8 )\n ~3.225","base.dists.triangular.skewness":"\nbase.dists.triangular.skewness( a, b, c )\n Returns the skewness of a triangular distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.triangular.skewness( 0.0, 1.0, 0.8 )\n ~-0.476\n > v = base.dists.triangular.skewness( 4.0, 12.0, 5.0 )\n ~0.532\n > v = base.dists.triangular.skewness( 2.0, 8.0, 5.0 )\n 0.0\n\n","base.dists.triangular.stdev":"\nbase.dists.triangular.stdev( a, b, c )\n Returns the standard deviation of a triangular distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.triangular.stdev( 0.0, 1.0, 0.8 )\n ~0.216\n > v = base.dists.triangular.stdev( 4.0, 12.0, 5.0 )\n ~1.78\n > v = base.dists.triangular.stdev( 2.0, 8.0, 5.0 )\n ~1.225\n\n","base.dists.triangular.Triangular":"\nbase.dists.triangular.Triangular( [a, b, c] )\n Returns a triangular distribution object.\n\n Parameters\n ----------\n a: number (optional)\n Minimum support. Must be smaller than `b` and `c`. Default: `0.0`.\n\n b: number (optional)\n Maximum support. Must be greater than `a` and `c`. Default: `1.0`.\n\n c: number (optional)\n Mode. Must be greater than `a` and smaller than `b`. Default: `0.5`.\n\n Returns\n -------\n triangular: Object\n Distribution instance.\n\n triangular.a: number\n Minimum support. If set, the value must be smaller or equal to `b` and\n `c`.\n\n triangular.b: number\n Maximum support. If set, the value must be greater than or equal to `a`\n and `c`.\n\n triangular.c: number\n Mode. If set, the value must be greater than or equal to `a` and smaller\n than or equal to `b`.\n\n triangular.entropy: number\n Read-only property which returns the differential entropy.\n\n triangular.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n triangular.mean: number\n Read-only property which returns the expected value.\n\n triangular.median: number\n Read-only property which returns the median.\n\n triangular.mode: number\n Read-only property which returns the mode.\n\n triangular.skewness: number\n Read-only property which returns the skewness.\n\n triangular.stdev: number\n Read-only property which returns the standard deviation.\n\n triangular.variance: number\n Read-only property which returns the variance.\n\n triangular.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n triangular.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n triangular.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n triangular.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n triangular.pdf: Function\n Evaluates the probability density function (PDF).\n\n triangular.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var triangular = base.dists.triangular.Triangular( 0.0, 1.0, 0.5 );\n > triangular.a\n 0.0\n > triangular.b\n 1.0\n > triangular.c\n 0.5\n > triangular.entropy\n ~-0.193\n > triangular.kurtosis\n -0.6\n > triangular.mean\n 0.5\n > triangular.median\n 0.5\n > triangular.mode\n 0.5\n > triangular.skewness\n 0.0\n > triangular.stdev\n ~0.204\n > triangular.variance\n ~0.042\n > triangular.cdf( 0.8 )\n 0.92\n > triangular.logcdf( 0.8 )\n ~-0.083\n > triangular.logpdf( 0.8 )\n ~-0.223\n > triangular.mgf( 0.8 )\n ~1.512\n > triangular.pdf( 0.8 )\n ~0.8\n > triangular.quantile( 0.8 )\n ~0.684\n\n","base.dists.triangular.variance":"\nbase.dists.triangular.variance( a, b, c )\n Returns the variance of a triangular distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If the condition `a <= c <= b` is not satisfied, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n c: number\n Mode.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.triangular.variance( 0.0, 1.0, 0.8 )\n ~0.047\n > v = base.dists.triangular.variance( 4.0, 12.0, 5.0 )\n ~3.167\n > v = base.dists.triangular.variance( 2.0, 8.0, 5.0 )\n ~1.5\n\n","base.dists.uniform.cdf":"\nbase.dists.uniform.cdf( x, a, b )\n Evaluates the cumulative distribution function (CDF) for a uniform\n distribution with minimum support `a` and maximum support `b` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.uniform.cdf( 9.0, 0.0, 10.0 )\n 0.9\n > y = base.dists.uniform.cdf( 0.5, 0.0, 2.0 )\n 0.25\n > y = base.dists.uniform.cdf( PINF, 2.0, 4.0 )\n 1.0\n > y = base.dists.uniform.cdf( NINF, 2.0, 4.0 )\n 0.0\n > y = base.dists.uniform.cdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.uniform.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.uniform.cdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.uniform.cdf( 2.0, 1.0, 0.0 )\n NaN\n\n\nbase.dists.uniform.cdf.factory( a, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a uniform distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.uniform.cdf.factory( 0.0, 10.0 );\n > var y = mycdf( 0.5 )\n 0.05\n > y = mycdf( 8.0 )\n 0.8\n\n","base.dists.uniform.cdf.factory":"\nbase.dists.uniform.cdf.factory( a, b )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a uniform distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mycdf = base.dists.uniform.cdf.factory( 0.0, 10.0 );\n > var y = mycdf( 0.5 )\n 0.05\n > y = mycdf( 8.0 )\n 0.8","base.dists.uniform.entropy":"\nbase.dists.uniform.entropy( a, b )\n Returns the differential entropy of a uniform distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Differential entropy.\n\n Examples\n --------\n > var v = base.dists.uniform.entropy( 0.0, 1.0 )\n 0.0\n > v = base.dists.uniform.entropy( 4.0, 12.0 )\n ~2.079\n > v = base.dists.uniform.entropy( 2.0, 8.0 )\n ~1.792\n\n","base.dists.uniform.kurtosis":"\nbase.dists.uniform.kurtosis( a, b )\n Returns the excess kurtosis of a uniform distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.uniform.kurtosis( 0.0, 1.0 )\n -1.2\n > v = base.dists.uniform.kurtosis( 4.0, 12.0 )\n -1.2\n > v = base.dists.uniform.kurtosis( 2.0, 8.0 )\n -1.2\n\n","base.dists.uniform.logcdf":"\nbase.dists.uniform.logcdf( x, a, b )\n Evaluates the logarithm of the cumulative distribution function (CDF) for a\n uniform distribution with minimum support `a` and maximum support `b` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.uniform.logcdf( 9.0, 0.0, 10.0 )\n ~-0.105\n > y = base.dists.uniform.logcdf( 0.5, 0.0, 2.0 )\n ~-1.386\n > y = base.dists.uniform.logcdf( PINF, 2.0, 4.0 )\n 0.0\n > y = base.dists.uniform.logcdf( NINF, 2.0, 4.0 )\n -Infinity\n > y = base.dists.uniform.logcdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.uniform.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.uniform.logcdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.uniform.logcdf( 2.0, 1.0, 0.0 )\n NaN\n\n\nbase.dists.uniform.logcdf.factory( a, b )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a uniform distribution with minimum support\n `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n logcdf: Function\n Logarithm of Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.uniform.logcdf.factory( 0.0, 10.0 );\n > var y = mylogcdf( 0.5 )\n ~-2.996\n > y = mylogcdf( 8.0 )\n ~-0.223\n\n","base.dists.uniform.logcdf.factory":"\nbase.dists.uniform.logcdf.factory( a, b )\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a uniform distribution with minimum support\n `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n logcdf: Function\n Logarithm of Cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.uniform.logcdf.factory( 0.0, 10.0 );\n > var y = mylogcdf( 0.5 )\n ~-2.996\n > y = mylogcdf( 8.0 )\n ~-0.223","base.dists.uniform.logpdf":"\nbase.dists.uniform.logpdf( x, a, b )\n Evaluates the logarithm of the probability density function (PDF) for a\n uniform distribution with minimum support `a` and maximum support `b` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.uniform.logpdf( 2.0, 0.0, 4.0 )\n ~-1.386\n > y = base.dists.uniform.logpdf( 5.0, 0.0, 4.0 )\n -infinity\n > y = base.dists.uniform.logpdf( 0.25, 0.0, 1.0 )\n 0.0\n > y = base.dists.uniform.logpdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.uniform.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.uniform.logpdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.uniform.logpdf( 2.0, 3.0, 1.0 )\n NaN\n\n\nbase.dists.uniform.logpdf.factory( a, b )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a uniform distribution with minimum support `a` and\n maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.uniform.logpdf.factory( 6.0, 7.0 );\n > var y = mylogPDF( 7.0 )\n 0.0\n > y = mylogPDF( 5.0 )\n -infinity\n\n","base.dists.uniform.logpdf.factory":"\nbase.dists.uniform.logpdf.factory( a, b )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a uniform distribution with minimum support `a` and\n maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylogPDF = base.dists.uniform.logpdf.factory( 6.0, 7.0 );\n > var y = mylogPDF( 7.0 )\n 0.0\n > y = mylogPDF( 5.0 )\n -infinity","base.dists.uniform.mean":"\nbase.dists.uniform.mean( a, b )\n Returns the expected value of a uniform distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.uniform.mean( 0.0, 1.0 )\n 0.5\n > v = base.dists.uniform.mean( 4.0, 12.0 )\n 8.0\n > v = base.dists.uniform.mean( 2.0, 8.0 )\n 5.0\n\n","base.dists.uniform.median":"\nbase.dists.uniform.median( a, b )\n Returns the median of a uniform distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.uniform.median( 0.0, 1.0 )\n 0.5\n > v = base.dists.uniform.median( 4.0, 12.0 )\n 8.0\n > v = base.dists.uniform.median( 2.0, 8.0 )\n 5.0\n\n","base.dists.uniform.mgf":"\nbase.dists.uniform.mgf( t, a, b )\n Evaluates the moment-generating function (MGF) for a uniform\n distribution with minimum support `a` and maximum support `b` at a value\n `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n t: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.uniform.mgf( 2.0, 0.0, 4.0 )\n ~372.495\n > y = base.dists.uniform.mgf( -0.2, 0.0, 4.0 )\n ~0.688\n > y = base.dists.uniform.mgf( 2.0, 0.0, 1.0 )\n ~3.195\n > y = base.dists.uniform.mgf( 0.5, 3.0, 2.0 )\n NaN\n > y = base.dists.uniform.mgf( 0.5, 3.0, 3.0 )\n NaN\n > y = base.dists.uniform.mgf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.uniform.mgf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.uniform.mgf( 0.0, 0.0, NaN )\n NaN\n\n\nbase.dists.uniform.mgf.factory( a, b )\n Returns a function for evaluating the moment-generating function (MGF)\n of a uniform distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.uniform.mgf.factory( 6.0, 7.0 );\n > var y = mymgf( 0.1 )\n ~1.916\n > y = mymgf( 1.1 )\n ~1339.321\n\n","base.dists.uniform.mgf.factory":"\nbase.dists.uniform.mgf.factory( a, b )\n Returns a function for evaluating the moment-generating function (MGF)\n of a uniform distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var mymgf = base.dists.uniform.mgf.factory( 6.0, 7.0 );\n > var y = mymgf( 0.1 )\n ~1.916\n > y = mymgf( 1.1 )\n ~1339.321","base.dists.uniform.pdf":"\nbase.dists.uniform.pdf( x, a, b )\n Evaluates the probability density function (PDF) for a uniform distribution\n with minimum support `a` and maximum support `b` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.uniform.pdf( 2.0, 0.0, 4.0 )\n 0.25\n > y = base.dists.uniform.pdf( 5.0, 0.0, 4.0 )\n 0.0\n > y = base.dists.uniform.pdf( 0.25, 0.0, 1.0 )\n 1.0\n > y = base.dists.uniform.pdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.uniform.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.uniform.pdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.uniform.pdf( 2.0, 3.0, 1.0 )\n NaN\n\n\nbase.dists.uniform.pdf.factory( a, b )\n Returns a function for evaluating the probability density function (PDF) of\n a uniform distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.uniform.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 7.0 )\n 1.0\n > y = myPDF( 5.0 )\n 0.0\n\n","base.dists.uniform.pdf.factory":"\nbase.dists.uniform.pdf.factory( a, b )\n Returns a function for evaluating the probability density function (PDF) of\n a uniform distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.uniform.pdf.factory( 6.0, 7.0 );\n > var y = myPDF( 7.0 )\n 1.0\n > y = myPDF( 5.0 )\n 0.0","base.dists.uniform.quantile":"\nbase.dists.uniform.quantile( p, a, b )\n Evaluates the quantile function for a uniform distribution with minimum\n support `a` and maximum support `b` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.uniform.quantile( 0.8, 0.0, 1.0 )\n 0.8\n > y = base.dists.uniform.quantile( 0.5, 0.0, 10.0 )\n 5.0\n\n > y = base.dists.uniform.quantile( 1.1, 0.0, 1.0 )\n NaN\n > y = base.dists.uniform.quantile( -0.2, 0.0, 1.0 )\n NaN\n\n > y = base.dists.uniform.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.uniform.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.uniform.quantile( 0.0, 0.0, NaN )\n NaN\n\n > y = base.dists.uniform.quantile( 0.5, 2.0, 1.0 )\n NaN\n\n\nbase.dists.uniform.quantile.factory( a, b )\n Returns a function for evaluating the quantile function of a uniform\n distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.uniform.quantile.factory( 0.0, 4.0 );\n > var y = myQuantile( 0.8 )\n 3.2\n\n","base.dists.uniform.quantile.factory":"\nbase.dists.uniform.quantile.factory( a, b )\n Returns a function for evaluating the quantile function of a uniform\n distribution with minimum support `a` and maximum support `b`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.uniform.quantile.factory( 0.0, 4.0 );\n > var y = myQuantile( 0.8 )\n 3.2","base.dists.uniform.skewness":"\nbase.dists.uniform.skewness( a, b )\n Returns the skewness of a uniform distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.uniform.skewness( 0.0, 1.0 )\n 0.0\n > v = base.dists.uniform.skewness( 4.0, 12.0 )\n 0.0\n > v = base.dists.uniform.skewness( 2.0, 8.0 )\n 0.0\n\n","base.dists.uniform.stdev":"\nbase.dists.uniform.stdev( a, b )\n Returns the standard deviation of a uniform distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.uniform.stdev( 0.0, 1.0 )\n ~0.289\n > v = base.dists.uniform.stdev( 4.0, 12.0 )\n ~2.309\n > v = base.dists.uniform.stdev( 2.0, 8.0 )\n ~1.732\n\n","base.dists.uniform.Uniform":"\nbase.dists.uniform.Uniform( [a, b] )\n Returns a uniform distribution object.\n\n Parameters\n ----------\n a: number (optional)\n Minimum support. Must be smaller than `b`. Default: `0.0`.\n\n b: number (optional)\n Maximum support. Must be greater than `a`. Default: `1.0`.\n\n Returns\n -------\n uniform: Object\n Distribution instance.\n\n uniform.a: number\n Minimum support. If set, the value must be smaller than `b`.\n\n uniform.b: number\n Maximum support. If set, the value must be greater than `a`.\n\n uniform.entropy: number\n Read-only property which returns the differential entropy.\n\n uniform.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n uniform.mean: number\n Read-only property which returns the expected value.\n\n uniform.median: number\n Read-only property which returns the median.\n\n uniform.skewness: number\n Read-only property which returns the skewness.\n\n uniform.stdev: number\n Read-only property which returns the standard deviation.\n\n uniform.variance: number\n Read-only property which returns the variance.\n\n uniform.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n uniform.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n uniform.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n uniform.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n uniform.pdf: Function\n Evaluates the probability density function (PDF).\n\n uniform.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var uniform = base.dists.uniform.Uniform( 0.0, 1.0 );\n > uniform.a\n 0.0\n > uniform.b\n 1.0\n > uniform.entropy\n 0.0\n > uniform.kurtosis\n -1.2\n > uniform.mean\n 0.5\n > uniform.median\n 0.5\n > uniform.skewness\n 0.0\n > uniform.stdev\n ~0.289\n > uniform.variance\n ~0.083\n > uniform.cdf( 0.8 )\n 0.8\n > uniform.logcdf( 0.5 )\n ~-0.693\n > uniform.logpdf( 1.0 )\n ~-0.0\n > uniform.mgf( 0.8 )\n ~1.532\n > uniform.pdf( 0.8 )\n 1.0\n > uniform.quantile( 0.8 )\n 0.8\n\n","base.dists.uniform.variance":"\nbase.dists.uniform.variance( a, b )\n Returns the variance of a uniform distribution.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `a >= b`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.uniform.variance( 0.0, 1.0 )\n ~0.083\n > v = base.dists.uniform.variance( 4.0, 12.0 )\n ~5.333\n > v = base.dists.uniform.variance( 2.0, 8.0 )\n 3.0\n\n","base.dists.weibull.cdf":"\nbase.dists.weibull.cdf( x, k, λ )\n Evaluates the cumulative distribution function (CDF) for a Weibull\n distribution with shape parameter `k` and scale parameter `λ` at a value\n `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a non-positive value for `λ` or `k`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated CDF.\n\n Examples\n --------\n > var y = base.dists.weibull.cdf( 2.0, 1.0, 1.0 )\n ~0.865\n > y = base.dists.weibull.cdf( -1.0, 2.0, 2.0 )\n 0.0\n > y = base.dists.weibull.cdf( PINF, 4.0, 2.0 )\n 1.0\n > y = base.dists.weibull.cdf( NINF, 4.0, 2.0 )\n 0.0\n > y = base.dists.weibull.cdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.weibull.cdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.weibull.cdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.weibull.cdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.weibull.cdf.factory( k, λ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Weibull distribution with shape parameter `k` and scale parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.weibull.cdf.factory( 2.0, 10.0 );\n > var y = myCDF( 12.0 )\n ~0.763\n\n","base.dists.weibull.cdf.factory":"\nbase.dists.weibull.cdf.factory( k, λ )\n Returns a function for evaluating the cumulative distribution function (CDF)\n of a Weibull distribution with shape parameter `k` and scale parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n cdf: Function\n Cumulative distribution function (CDF).\n\n Examples\n --------\n > var myCDF = base.dists.weibull.cdf.factory( 2.0, 10.0 );\n > var y = myCDF( 12.0 )\n ~0.763","base.dists.weibull.entropy":"\nbase.dists.weibull.entropy( k, λ )\n Returns the differential entropy of a Weibull distribution (in nats).\n\n If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Entropy.\n\n Examples\n --------\n > var v = base.dists.weibull.entropy( 1.0, 1.0 )\n 1.0\n > v = base.dists.weibull.entropy( 4.0, 12.0 )\n ~2.532\n > v = base.dists.weibull.entropy( 8.0, 2.0 )\n ~0.119\n\n","base.dists.weibull.kurtosis":"\nbase.dists.weibull.kurtosis( k, λ )\n Returns the excess kurtosis of a Weibull distribution.\n\n If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Excess kurtosis.\n\n Examples\n --------\n > var v = base.dists.weibull.kurtosis( 1.0, 1.0 )\n 6.0\n > v = base.dists.weibull.kurtosis( 4.0, 12.0 )\n ~-0.252\n > v = base.dists.weibull.kurtosis( 8.0, 2.0 )\n ~0.328\n\n","base.dists.weibull.logcdf":"\nbase.dists.weibull.logcdf( x, k, λ )\n Evaluates the logarithm of the cumulative distribution function (CDF) for a\n Weibull distribution with shape parameter `k` and scale parameter `λ` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a nonpositive value for `λ` or `k`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logCDF.\n\n Examples\n --------\n > var y = base.dists.weibull.logcdf( 2.0, 1.0, 1.0 )\n ~-0.145\n > y = base.dists.weibull.logcdf( -1.0, 2.0, 2.0 )\n -Infinity\n > y = base.dists.weibull.logcdf( PINF, 4.0, 2.0 )\n 0.0\n > y = base.dists.weibull.logcdf( NINF, 4.0, 2.0 )\n -Infinity\n > y = base.dists.weibull.logcdf( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.weibull.logcdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.weibull.logcdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.weibull.logcdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.weibull.logcdf.factory( k, λ)\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Weibull distribution with scale parameter\n `λ` and shape parameter `k`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.weibull.logcdf.factory( 2.0, 10.0 );\n > var y = mylogcdf( 12.0 )\n ~-0.27\n\n","base.dists.weibull.logcdf.factory":"\nbase.dists.weibull.logcdf.factory( k, λ)\n Returns a function for evaluating the logarithm of the cumulative\n distribution function (CDF) of a Weibull distribution with scale parameter\n `λ` and shape parameter `k`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n logcdf: Function\n Logarithm of cumulative distribution function (CDF).\n\n Examples\n --------\n > var mylogcdf = base.dists.weibull.logcdf.factory( 2.0, 10.0 );\n > var y = mylogcdf( 12.0 )\n ~-0.27","base.dists.weibull.logpdf":"\nbase.dists.weibull.logpdf( x, k, λ )\n Evaluates the logarithm of the probability density function (PDF) for a\n Weibull distribution with shape parameter `k` and scale parameter `λ` at a\n value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a nonpositive value for `λ` or `k`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated logPDF.\n\n Examples\n --------\n > var y = base.dists.weibull.logpdf( 2.0, 1.0, 0.5 )\n ~-3.307\n > y = base.dists.weibull.logpdf( 0.1, 1.0, 1.0 )\n ~-0.1\n > y = base.dists.weibull.logpdf( -1.0, 4.0, 2.0 )\n -Infinity\n > y = base.dists.weibull.logpdf( NaN, 0.6, 1.0 )\n NaN\n > y = base.dists.weibull.logpdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.weibull.logpdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.weibull.logpdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.weibull.logpdf.factory( k, λ )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Weibull distribution with shape parameter `k` and scale\n parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylofpdf = base.dists.weibull.logpdf.factory( 7.0, 6.0 );\n > y = mylofpdf( 7.0 )\n ~-1.863\n\n","base.dists.weibull.logpdf.factory":"\nbase.dists.weibull.logpdf.factory( k, λ )\n Returns a function for evaluating the logarithm of the probability density\n function (PDF) of a Weibull distribution with shape parameter `k` and scale\n parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n logpdf: Function\n Logarithm of probability density function (PDF).\n\n Examples\n --------\n > var mylofpdf = base.dists.weibull.logpdf.factory( 7.0, 6.0 );\n > y = mylofpdf( 7.0 )\n ~-1.863","base.dists.weibull.mean":"\nbase.dists.weibull.mean( k, λ )\n Returns the expected value of a Weibull distribution.\n\n If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Expected value.\n\n Examples\n --------\n > var v = base.dists.weibull.mean( 1.0, 1.0 )\n 1.0\n > v = base.dists.weibull.mean( 4.0, 12.0 )\n ~10.877\n > v = base.dists.weibull.mean( 8.0, 2.0 )\n ~1.883\n\n","base.dists.weibull.median":"\nbase.dists.weibull.median( k, λ )\n Returns the median of a Weibull distribution.\n\n If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Median.\n\n Examples\n --------\n > var v = base.dists.weibull.median( 1.0, 1.0 )\n ~0.693\n > v = base.dists.weibull.median( 4.0, 12.0 )\n ~10.949\n > v = base.dists.weibull.median( 8.0, 2.0 )\n ~1.91\n\n","base.dists.weibull.mgf":"\nbase.dists.weibull.mgf( x, k, λ )\n Evaluates the moment-generating function (MGF) for a Weibull distribution\n with shape parameter `k` and scale parameter `λ` at a value `t`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a non-positive value for `λ` or `k`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated MGF.\n\n Examples\n --------\n > var y = base.dists.weibull.mgf( 1.0, 1.0, 0.5 )\n ~2.0\n > y = base.dists.weibull.mgf( -1.0, 4.0, 4.0 )\n ~0.019\n\n > y = base.dists.weibull.mgf( NaN, 1.0, 1.0 )\n NaN\n > y = base.dists.weibull.mgf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.weibull.mgf( 0.0, 1.0, NaN )\n NaN\n\n > y = base.dists.weibull.mgf( 0.2, -1.0, 0.5 )\n NaN\n > y = base.dists.weibull.mgf( 0.2, 0.0, 0.5 )\n NaN\n\n > y = base.dists.weibull.mgf( 0.2, 0.5, -1.0 )\n NaN\n > y = base.dists.weibull.mgf( 0.2, 0.5, 0.0 )\n NaN\n\n\nbase.dists.weibull.mgf.factory( k, λ )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Weibull distribution with shape parameter `k` and scale parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.weibull.mgf.factory( 8.0, 10.0 );\n > var y = myMGF( 0.8 )\n ~3150.149\n > y = myMGF( 0.08 )\n ~2.137\n\n","base.dists.weibull.mgf.factory":"\nbase.dists.weibull.mgf.factory( k, λ )\n Returns a function for evaluating the moment-generating function (MGF) of a\n Weibull distribution with shape parameter `k` and scale parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n mgf: Function\n Moment-generating function (MGF).\n\n Examples\n --------\n > var myMGF = base.dists.weibull.mgf.factory( 8.0, 10.0 );\n > var y = myMGF( 0.8 )\n ~3150.149\n > y = myMGF( 0.08 )\n ~2.137","base.dists.weibull.mode":"\nbase.dists.weibull.mode( k, λ )\n Returns the mode of a Weibull distribution.\n\n If `0 < k <= 1`, the function returns `0.0`.\n\n If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Mode.\n\n Examples\n --------\n > var v = base.dists.weibull.mode( 1.0, 1.0 )\n 0.0\n > v = base.dists.weibull.mode( 4.0, 12.0 )\n ~11.167\n > v = base.dists.weibull.mode( 8.0, 2.0 )\n ~1.967\n\n","base.dists.weibull.pdf":"\nbase.dists.weibull.pdf( x, k, λ )\n Evaluates the probability density function (PDF) for a Weibull distribution\n with shape parameter `k` and scale parameter `λ` at a value `x`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a nonpositive value for `λ` or `k`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated PDF.\n\n Examples\n --------\n > var y = base.dists.weibull.pdf( 2.0, 1.0, 0.5 )\n ~0.037\n > y = base.dists.weibull.pdf( 0.1, 1.0, 1.0 )\n ~0.905\n > y = base.dists.weibull.pdf( -1.0, 4.0, 2.0 )\n 0.0\n > y = base.dists.weibull.pdf( NaN, 0.6, 1.0 )\n NaN\n > y = base.dists.weibull.pdf( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.weibull.pdf( 0.0, 0.0, NaN )\n NaN\n > y = base.dists.weibull.pdf( 2.0, 0.0, -1.0 )\n NaN\n\n\nbase.dists.weibull.pdf.factory( k, λ )\n Returns a function for evaluating the probability density function (PDF) of\n a Weibull distribution with shape parameter `k` and scale parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.weibull.pdf.factory( 7.0, 6.0 );\n > var y = myPDF( 7.0 )\n ~0.155\n\n","base.dists.weibull.pdf.factory":"\nbase.dists.weibull.pdf.factory( k, λ )\n Returns a function for evaluating the probability density function (PDF) of\n a Weibull distribution with shape parameter `k` and scale parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n pdf: Function\n Probability density function (PDF).\n\n Examples\n --------\n > var myPDF = base.dists.weibull.pdf.factory( 7.0, 6.0 );\n > var y = myPDF( 7.0 )\n ~0.155","base.dists.weibull.quantile":"\nbase.dists.weibull.quantile( p, k, λ )\n Evaluates the quantile function for a Weibull distribution with scale\n parameter `k` and shape parameter `λ` at a probability `p`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided a nonpositive value for `λ` or `k`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input probability.\n\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Evaluated quantile function.\n\n Examples\n --------\n > var y = base.dists.weibull.quantile( 0.8, 1.0, 1.0 )\n ~1.609\n > y = base.dists.weibull.quantile( 0.5, 2.0, 4.0 )\n ~3.33\n\n > y = base.dists.weibull.quantile( 1.1, 1.0, 1.0 )\n NaN\n > y = base.dists.weibull.quantile( -0.2, 1.0, 1.0 )\n NaN\n\n > y = base.dists.weibull.quantile( NaN, 0.0, 1.0 )\n NaN\n > y = base.dists.weibull.quantile( 0.0, NaN, 1.0 )\n NaN\n > y = base.dists.weibull.quantile( 0.0, 0.0, NaN )\n NaN\n\n > y = base.dists.weibull.quantile( 0.5, 1.0, -1.0 )\n NaN\n\n\nbase.dists.weibull.quantile.factory( k, λ )\n Returns a function for evaluating the quantile function of a Weibull\n distribution with scale parameter `k` and shape parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.weibull.quantile.factory( 2.0, 10.0 );\n > var y = myQuantile( 0.4 )\n ~7.147\n\n","base.dists.weibull.quantile.factory":"\nbase.dists.weibull.quantile.factory( k, λ )\n Returns a function for evaluating the quantile function of a Weibull\n distribution with scale parameter `k` and shape parameter `λ`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n quantile: Function\n Quantile function.\n\n Examples\n --------\n > var myQuantile = base.dists.weibull.quantile.factory( 2.0, 10.0 );\n > var y = myQuantile( 0.4 )\n ~7.147","base.dists.weibull.skewness":"\nbase.dists.weibull.skewness( k, λ )\n Returns the skewness of a Weibull distribution.\n\n If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Skewness.\n\n Examples\n --------\n > var v = base.dists.weibull.skewness( 1.0, 1.0 )\n 2.0\n > v = base.dists.weibull.skewness( 4.0, 12.0 )\n ~-0.087\n > v = base.dists.weibull.skewness( 8.0, 2.0 )\n ~-0.534\n\n","base.dists.weibull.stdev":"\nbase.dists.weibull.stdev( k, λ )\n Returns the standard deviation of a Weibull distribution.\n\n If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Standard deviation.\n\n Examples\n --------\n > var v = base.dists.weibull.stdev( 1.0, 1.0 )\n 1.0\n > v = base.dists.weibull.stdev( 4.0, 12.0 )\n ~3.051\n > v = base.dists.weibull.stdev( 8.0, 2.0 )\n ~0.279\n\n","base.dists.weibull.variance":"\nbase.dists.weibull.variance( k, λ )\n Returns the variance of a Weibull distribution.\n\n If `k <= 0` or `λ <= 0`, the function returns `NaN`.\n\n If `k` or `λ` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n k: number\n Shape parameter.\n\n λ: number\n Scale parameter.\n\n Returns\n -------\n out: number\n Variance.\n\n Examples\n --------\n > var v = base.dists.weibull.variance( 1.0, 1.0 )\n 1.0\n > v = base.dists.weibull.variance( 4.0, 12.0 )\n ~9.311\n > v = base.dists.weibull.variance( 8.0, 2.0 )\n ~0.078\n\n","base.dists.weibull.Weibull":"\nbase.dists.weibull.Weibull( [k, λ] )\n Returns a Weibull distribution object.\n\n Parameters\n ----------\n k: number (optional)\n Shape parameter. Must be greater than `0`. Default: `1.0`.\n\n λ: number (optional)\n Scale parameter. Must be greater than `0`. Default: `1.0`.\n\n Returns\n -------\n weibull: Object\n Distribution instance.\n\n weibull.k: number\n Shape parameter. If set, the value must be greater than `0`.\n\n weibull.lambda: number\n Scale parameter. If set, the value must be greater than `0`.\n\n weibull.entropy: number\n Read-only property which returns the differential entropy.\n\n weibull.kurtosis: number\n Read-only property which returns the excess kurtosis.\n\n weibull.mean: number\n Read-only property which returns the expected value.\n\n weibull.median: number\n Read-only property which returns the median.\n\n weibull.mode: number\n Read-only property which returns the mode.\n\n weibull.skewness: number\n Read-only property which returns the skewness.\n\n weibull.stdev: number\n Read-only property which returns the standard deviation.\n\n weibull.variance: number\n Read-only property which returns the variance.\n\n weibull.cdf: Function\n Evaluates the cumulative distribution function (CDF).\n\n weibull.logcdf: Function\n Evaluates the natural logarithm of the cumulative distribution function\n (CDF).\n\n weibull.logpdf: Function\n Evaluates the natural logarithm of the probability density function\n (PDF).\n\n weibull.mgf: Function\n Evaluates the moment-generating function (MGF).\n\n weibull.pdf: Function\n Evaluates the probability density function (PDF).\n\n weibull.quantile: Function\n Evaluates the quantile function at probability `p`.\n\n Examples\n --------\n > var weibull = base.dists.weibull.Weibull( 6.0, 5.0 );\n > weibull.k\n 6.0\n > weibull.lambda\n 5.0\n > weibull.entropy\n ~1.299\n > weibull.kurtosis\n ~0.035\n > weibull.mean\n ~4.639\n > weibull.median\n ~4.704\n > weibull.mode\n ~4.85\n > weibull.skewness\n ~-0.373\n > weibull.stdev\n ~0.899\n > weibull.variance\n ~0.808\n > weibull.cdf( 3.0 )\n ~0.046\n > weibull.logcdf( 3.0 )\n ~-3.088\n > weibull.logpdf( 1.0 )\n ~-7.865\n > weibull.mgf( -0.5 )\n ~0.075\n > weibull.pdf( 3.0 )\n ~0.089\n > weibull.quantile( 0.8 )\n ~5.413\n\n","base.ellipe":"\nbase.ellipe( m )\n Computes the complete elliptic integral of the second kind.\n\n Parameters\n ----------\n m: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.ellipe( 0.5 )\n ~1.351\n > y = base.ellipe( -1.0 )\n ~1.910\n > y = base.ellipe( 2.0 )\n NaN\n > y = base.ellipe( PINF )\n NaN\n > y = base.ellipe( NINF )\n NaN\n > y = base.ellipe( NaN )\n NaN\n\n See Also\n --------\n base.ellipj, base.ellipk\n","base.ellipj":"\nbase.ellipj( u, m )\n Computes the Jacobi elliptic functions sn, cn, and dn and Jacobi\n amplitude am.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n Returns\n -------\n out: Array\n Jacobi elliptic functions and Jacobi amplitude.\n\n Examples\n --------\n > var v = base.ellipj( 0.3, 0.5 )\n [ ~0.293, ~0.956, ~0.978, ~0.298 ]\n > v = base.ellipj( 0.0, 0.0 )\n [ ~0.0, ~1.0, ~1.0, ~0.0 ]\n > v = base.ellipj( Infinity, 1.0 )\n [ ~1.0, ~0.0, ~0.0, ~1.571 ]\n > v = base.ellipj( 0.0, -2.0)\n [ ~0.0, ~1.0, ~1.0, NaN ]\n > v = base.ellipj( NaN, NaN )\n [ NaN, NaN, NaN, NaN ]\n\n\nbase.ellipj.assign( u, m, out, stride, offset )\n Computes the Jacobi elliptic functions sn, cn, and dn and Jacobi\n amplitude am and assigns results to a provided output array.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Jacobi elliptic functions and Jacobi amplitude.\n\n Examples\n --------\n > var out = new Float64Array( 4 );\n > var v = base.ellipj.assign( 0.3, 0.5, out, 1, 0 )\n [ ~0.293, ~0.956, ~0.978, ~0.298 ]\n > var bool = ( v === out )\n true\n\n\nbase.ellipj.sn( u, m )\n Computes the Jacobi elliptic function sn.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n Returns\n -------\n out: number\n Jacobi elliptic function sn.\n\n Examples\n --------\n > var v = base.ellipj.sn( 0.3, 0.5 )\n ~0.293\n\n\nbase.ellipj.cn( u, m )\n Computes the Jacobi elliptic functions cn.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n Returns\n -------\n out: number\n Jacobi elliptic function cn.\n\n Examples\n --------\n > var v = base.ellipj.cn( 0.3, 0.5 )\n ~0.956\n\n\nbase.ellipj.dn( u, m )\n Computes the Jacobi elliptic function dn.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n Returns\n -------\n out: number\n Jacobi elliptic function dn.\n\n Examples\n --------\n > var v = base.ellipj.dn( 0.3, 0.5 )\n ~0.978\n\n\nbase.ellipj.am( u, m )\n Computes the Jacobi amplitude am.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n Returns\n -------\n out: number\n Jacobi elliptic function am.\n\n Examples\n --------\n > var v = base.ellipj.am( 0.3, 0.5 )\n ~0.298\n\n See Also\n --------\n base.ellipe, base.ellipk","base.ellipj.assign":"\nbase.ellipj.assign( u, m, out, stride, offset )\n Computes the Jacobi elliptic functions sn, cn, and dn and Jacobi\n amplitude am and assigns results to a provided output array.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Jacobi elliptic functions and Jacobi amplitude.\n\n Examples\n --------\n > var out = new Float64Array( 4 );\n > var v = base.ellipj.assign( 0.3, 0.5, out, 1, 0 )\n [ ~0.293, ~0.956, ~0.978, ~0.298 ]\n > var bool = ( v === out )\n true","base.ellipj.sn":"\nbase.ellipj.sn( u, m )\n Computes the Jacobi elliptic function sn.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n Returns\n -------\n out: number\n Jacobi elliptic function sn.\n\n Examples\n --------\n > var v = base.ellipj.sn( 0.3, 0.5 )\n ~0.293","base.ellipj.cn":"\nbase.ellipj.cn( u, m )\n Computes the Jacobi elliptic functions cn.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n Returns\n -------\n out: number\n Jacobi elliptic function cn.\n\n Examples\n --------\n > var v = base.ellipj.cn( 0.3, 0.5 )\n ~0.956","base.ellipj.dn":"\nbase.ellipj.dn( u, m )\n Computes the Jacobi elliptic function dn.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n Returns\n -------\n out: number\n Jacobi elliptic function dn.\n\n Examples\n --------\n > var v = base.ellipj.dn( 0.3, 0.5 )\n ~0.978","base.ellipj.am":"\nbase.ellipj.am( u, m )\n Computes the Jacobi amplitude am.\n\n Parameters\n ----------\n u: number\n Input value.\n\n m: number\n Modulus m, equivalent to k².\n\n Returns\n -------\n out: number\n Jacobi elliptic function am.\n\n Examples\n --------\n > var v = base.ellipj.am( 0.3, 0.5 )\n ~0.298\n\n See Also\n --------\n base.ellipe, base.ellipk","base.ellipk":"\nbase.ellipk( m )\n Computes the complete elliptic integral of the first kind.\n\n Parameters\n ----------\n m: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.ellipk( 0.5 )\n ~1.854\n > y = base.ellipk( -1.0 )\n ~1.311\n > y = base.ellipk( 2.0 )\n NaN\n > y = base.ellipk( PINF )\n NaN\n > y = base.ellipk( NINF )\n NaN\n > y = base.ellipk( NaN )\n NaN\n\n See Also\n --------\n base.ellipe, base.ellipj\n","base.endsWith":"\nbase.endsWith( str, search, len )\n Tests if a string ends with the characters of another string.\n\n If provided an empty search string, the function always returns `true`.\n\n Parameters\n ----------\n str: string\n Input string.\n\n search: string\n Search string.\n\n len: integer\n Substring length. Restricts the search to a substring within the input\n string beginning from the leftmost character. If provided a negative\n value, `len` indicates to ignore the last `len` characters, and is thus\n equivalent to `str.length + len`. Default: str.length.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether a string ends with the characters of another\n string.\n\n Examples\n --------\n > var bool = base.endsWith( 'beep', 'ep', 4 )\n true\n > bool = base.endsWith( 'Beep', 'op', 4 )\n false\n > bool = base.endsWith( 'Beep', 'ee', 3 )\n true\n > bool = base.endsWith( 'Beep', 'ee', -1 )\n true\n > bool = base.endsWith( 'beep', '', 4 )\n true\n\n See Also\n --------\n base.startsWith\n","base.epsdiff":"\nbase.epsdiff( x, y[, scale] )\n Computes the relative difference of two real numbers in units of double-\n precision floating-point epsilon.\n\n By default, the function scales the absolute difference by dividing the\n absolute difference by the maximum absolute value of `x` and `y`. To scale\n by a different function, specify a scale function name.\n\n The following `scale` functions are supported:\n\n - 'max-abs': maximum absolute value of `x` and `y` (default).\n - 'max': maximum value of `x` and `y`.\n - 'min-abs': minimum absolute value of `x` and `y`.\n - 'min': minimum value of `x` and `y`.\n - 'mean-abs': arithmetic mean of the absolute values of `x` and `y`.\n - 'mean': arithmetic mean of `x` and `y`.\n - 'x': `x` (*noncommutative*).\n - 'y': `y` (*noncommutative*).\n\n To use a custom scale function, provide a function which accepts two numeric\n arguments `x` and `y`.\n\n If computing the relative difference in units of epsilon will result in\n overflow, the function returns the maximum double-precision floating-point\n number.\n\n If the absolute difference of `x` and `y` is `0`, the relative difference is\n always `0`.\n\n If `|x| = |y| = infinity`, the function returns `NaN`.\n\n If `|x| = |-y| = infinity`, the relative difference is `+infinity`.\n\n If a `scale` function returns `0`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n scale: string|Function (optional)\n Scale function. Default: `'max-abs'`.\n\n Returns\n -------\n out: number\n Relative difference in units of double-precision floating-point epsilon.\n\n Examples\n --------\n > var d = base.epsdiff( 12.15, 12.149999999999999 )\n ~0.658\n > d = base.epsdiff( 2.4341309458983933, 2.4341309458633909, 'mean-abs' )\n ~64761.512\n\n // Custom scale function:\n > function scale( x, y ) { return ( x > y ) ? y : x; };\n > d = base.epsdiff( 1.0000000000000002, 1.0000000000000100, scale )\n ~44\n\n See Also\n --------\n base.absdiff, base.reldiff\n","base.erf":"\nbase.erf( x )\n Evaluates the error function.\n\n If provided `NaN`, the function returns `NaN`.\n\n As the error function is an odd function (i.e., `erf(-x) == -erf(x)`), if\n provided `-0`, the function returns `-0`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.erf( 2.0 )\n ~0.9953\n > y = base.erf( -1.0 )\n ~-0.8427\n > y = base.erf( -0.0 )\n -0.0\n > y = base.erf( NaN )\n NaN\n\n See Also\n --------\n base.erfc, base.erfinv, base.erfcinv\n","base.erfc":"\nbase.erfc( x )\n Evaluates the complementary error function.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.erfc( 2.0 )\n ~0.0047\n > y = base.erfc( -1.0 )\n ~1.8427\n > y = base.erfc( 0.0 )\n 1.0\n > y = base.erfc( PINF )\n 0.0\n > y = base.erfc( NINF )\n 2.0\n > y = base.erfc( NaN )\n NaN\n\n See Also\n --------\n base.erf, base.erfinv, base.erfcinv, base.erfcx\n","base.erfcinv":"\nbase.erfcinv( x )\n Evaluates the inverse complementary error function.\n\n The domain of `x` is restricted to `[0,2]`. If `x` is outside this interval,\n the function returns `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.erfcinv( 0.5 )\n ~0.4769\n > y = base.erfcinv( 0.8 )\n ~0.1791\n > y = base.erfcinv( 0.0 )\n Infinity\n > y = base.erfcinv( 2.0 )\n -Infinity\n > y = base.erfcinv( NaN )\n NaN\n\n See Also\n --------\n base.erf, base.erfc, base.erfinv, base.erfcx\n","base.erfcx":"\nbase.erfcx( x )\n Evaluates the scaled complementary error function.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.erfcx( 1.0 )\n ~0.4276\n > y = base.erfcx( -1.0 )\n ~5.01\n > y = base.erfcx( 0.0 )\n 1.0\n > y = base.erfcx( NaN )\n NaN\n\n See Also\n --------\n base.erfc, base.erfcinv, base.erf, base.erfinv","base.erfinv":"\nbase.erfinv( x )\n Evaluates the inverse error function.\n\n If `|x| > 1`, the function returns `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n As the inverse error function is an odd function (i.e., `erfinv(-x) ==\n -erfinv(x)`), if provided `-0`, the function returns `-0`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.erfinv( 0.5 )\n ~0.4769\n > y = base.erfinv( 0.8 )\n ~0.9062\n > y = base.erfinv( 0.0 )\n 0.0\n > y = base.erfinv( -0.0 )\n -0.0\n > y = base.erfinv( -1.0 )\n -Infinity\n > y = base.erfinv( 1.0 )\n Infinity\n > y = base.erfinv( NaN )\n NaN\n\n See Also\n --------\n base.erf, base.erfc, base.erfcinv\n","base.eta":"\nbase.eta( s )\n Evaluates the Dirichlet eta function as a function of a real variable `s`.\n\n Parameters\n ----------\n s: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.eta( 0.0 )\n 0.5\n > y = base.eta( -1.0 )\n 0.25\n > y = base.eta( 1.0 )\n ~0.6931\n > y = base.eta( 3.14 )\n ~0.9096\n > y = base.eta( NaN )\n NaN\n\n","base.evalpoly":"\nbase.evalpoly( c, x )\n Evaluates a polynomial using double-precision floating-point arithmetic.\n\n Parameters\n ----------\n c: Array\n Polynomial coefficients sorted in ascending degree.\n\n x: number\n Value at which to evaluate the polynomial.\n\n Returns\n -------\n out: number\n Evaluated polynomial.\n\n Examples\n --------\n > var arr = [ 3.0, 2.0, 1.0 ];\n\n // 3*10^0 + 2*10^1 + 1*10^2\n > var v = base.evalpoly( arr, 10.0 )\n 123.0\n\n\nbase.evalpoly.factory( c )\n Returns a function for evaluating a polynomial using double-precision\n floating-point arithmetic.\n\n Parameters\n ----------\n c: Array\n Polynomial coefficients sorted in ascending degree.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a polynomial.\n\n Examples\n --------\n > var f = base.evalpoly.factory( [ 3.0, 2.0, 1.0 ] );\n\n // 3*10^0 + 2*10^1 + 1*10^2\n > var v = f( 10.0 )\n 123.0\n\n // 3*5^0 + 2*5^1 + 1*5^2\n > v = f( 5.0 )\n 38.0\n\n See Also\n --------\n base.evalrational\n","base.evalpoly.factory":"\nbase.evalpoly.factory( c )\n Returns a function for evaluating a polynomial using double-precision\n floating-point arithmetic.\n\n Parameters\n ----------\n c: Array\n Polynomial coefficients sorted in ascending degree.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a polynomial.\n\n Examples\n --------\n > var f = base.evalpoly.factory( [ 3.0, 2.0, 1.0 ] );\n\n // 3*10^0 + 2*10^1 + 1*10^2\n > var v = f( 10.0 )\n 123.0\n\n // 3*5^0 + 2*5^1 + 1*5^2\n > v = f( 5.0 )\n 38.0\n\n See Also\n --------\n base.evalrational","base.evalrational":"\nbase.evalrational( P, Q, x )\n Evaluates a rational function using double-precision floating-point\n arithmetic.\n\n A rational function `f(x)` is defined as\n\n P(x)\n f(x) = ----\n Q(x)\n\n where both `P(x)` and `Q(x)` are polynomials in `x`.\n\n The coefficients for both `P` and `Q` should be sorted in ascending degree.\n\n For polynomials of different degree, the coefficient array for the lower\n degree polynomial should be padded with zeros.\n\n Parameters\n ----------\n P: Array\n Numerator polynomial coefficients sorted in ascending degree.\n\n Q: Array\n Denominator polynomial coefficients sorted in ascending degree.\n\n x: number\n Value at which to evaluate the rational function.\n\n Returns\n -------\n out: number\n Evaluated rational function.\n\n Examples\n --------\n // 2x^3 + 4x^2 - 5x^1 - 6x^0\n > var P = [ -6.0, -5.0, 4.0, 2.0 ];\n\n // 0.5x^1 + 3x^0\n > var Q = [ 3.0, 0.5, 0.0, 0.0 ]; // zero-padded\n\n // Evaluate the rational function:\n > var v = base.evalrational( P, Q, 6.0 )\n 90.0\n\n\nbase.evalrational.factory( P, Q )\n Returns a function for evaluating a rational function using double-precision\n floating-point arithmetic.\n\n Parameters\n ----------\n P: Array\n Numerator polynomial coefficients sorted in ascending degree.\n\n Q: Array\n Denominator polynomial coefficients sorted in ascending degree.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a rational function.\n\n Examples\n --------\n > var P = [ 20.0, 8.0, 3.0 ];\n > var Q = [ 10.0, 9.0, 1.0 ];\n > var f = base.evalrational.factory( P, Q );\n\n // (20*10^0 + 8*10^1 + 3*10^2) / (10*10^0 + 9*10^1 + 1*10^2):\n > var v = f( 10.0 )\n 2.0\n\n // (20*2^0 + 8*2^1 + 3*2^2) / (10*2^0 + 9*2^1 + 1*2^2):\n > v = f( 2.0 )\n 1.5\n\n See Also\n --------\n base.evalpoly\n","base.evalrational.factory":"\nbase.evalrational.factory( P, Q )\n Returns a function for evaluating a rational function using double-precision\n floating-point arithmetic.\n\n Parameters\n ----------\n P: Array\n Numerator polynomial coefficients sorted in ascending degree.\n\n Q: Array\n Denominator polynomial coefficients sorted in ascending degree.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a rational function.\n\n Examples\n --------\n > var P = [ 20.0, 8.0, 3.0 ];\n > var Q = [ 10.0, 9.0, 1.0 ];\n > var f = base.evalrational.factory( P, Q );\n\n // (20*10^0 + 8*10^1 + 3*10^2) / (10*10^0 + 9*10^1 + 1*10^2):\n > var v = f( 10.0 )\n 2.0\n\n // (20*2^0 + 8*2^1 + 3*2^2) / (10*2^0 + 9*2^1 + 1*2^2):\n > v = f( 2.0 )\n 1.5\n\n See Also\n --------\n base.evalpoly","base.exp":"\nbase.exp( x )\n Evaluates the natural exponential function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.exp( 4.0 )\n ~54.5982\n > y = base.exp( -9.0 )\n ~1.234e-4\n > y = base.exp( 0.0 )\n 1.0\n > y = base.exp( NaN )\n NaN\n\n See Also\n --------\n base.exp10, base.exp2, base.expm1, base.ln\n","base.exp2":"\nbase.exp2( x )\n Evaluates the base 2 exponential function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.exp2( 3.0 )\n 8.0\n > y = base.exp2( -9.0 )\n ~0.002\n > y = base.exp2( 0.0 )\n 1.0\n > y = base.exp2( NaN )\n NaN\n\n See Also\n --------\n base.exp, base.exp10, base.log2\n","base.exp10":"\nbase.exp10( x )\n Evaluates the base 10 exponential function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.exp10( 3.0 )\n 1000\n > y = base.exp10( -9.0 )\n 1.0e-9\n > y = base.exp10( 0.0 )\n 1.0\n > y = base.exp10( NaN )\n NaN\n\n See Also\n --------\n base.exp, base.exp2, base.log10\n","base.expit":"\nbase.expit( x )\n Evaluates the standard logistic function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.expit( 0.0 )\n 0.5\n > y = base.expit( 1.0 )\n ~0.731\n > y = base.expit( -1.0 )\n ~0.269\n > y = base.expit( Infinity )\n 1.0\n > y = base.expit( NaN )\n NaN\n\n See Also\n --------\n base.exp, base.logit","base.expm1":"\nbase.expm1( x )\n Computes `exp(x)-1`, where `exp(x)` is the natural exponential function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.expm1( 0.2 )\n ~0.221\n > y = base.expm1( -9.0 )\n ~-1.0\n > y = base.expm1( 0.0 )\n 0.0\n > y = base.expm1( NaN )\n NaN\n\n See Also\n --------\n base.exp, base.expm1rel\n","base.expm1rel":"\nbase.expm1rel( x )\n Relative error exponential.\n\n When `x` is near zero,\n\n e^x - 1\n\n can suffer catastrophic cancellation (i.e., significant loss of precision).\n This function avoids the loss of precision when `x` is near zero.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.expm1rel( 0.0 )\n 1.0\n > y = base.expm1rel( 1.0 )\n ~1.718\n > y = base.expm1rel( -1.0 )\n ~0.632\n > y = base.expm1rel( NaN )\n NaN\n\t\n See Also\n --------\n base.exp, base.expm1\n","base.exponent":"\nbase.exponent( x )\n Returns an integer corresponding to the unbiased exponent of a double-\n precision floating-point number.\n\n Parameters\n ----------\n x: number\n Double-precision floating-point number.\n\n Returns\n -------\n out: integer\n Unbiased exponent.\n\n Examples\n --------\n > var exponent = base.exponent( 3.14e-307 )\n -1019\n > exponent = base.exponent( -3.14 )\n 1\n > exponent = base.exponent( 0.0 )\n -1023\n > exponent = base.exponent( NaN )\n 1024\n\n See Also\n --------\n base.exponentf\n","base.exponentf":"\nbase.exponentf( x )\n Returns an integer corresponding to the unbiased exponent of a single-\n precision floating-point number.\n\n Parameters\n ----------\n x: float\n Single-precision floating-point number.\n\n Returns\n -------\n out: integer\n Unbiased exponent.\n\n Examples\n --------\n > var exponent = base.exponentf( base.float64ToFloat32( 3.14e34 ) )\n 114\n > exponent = base.exponentf( base.float64ToFloat32( 3.14e-34 ) )\n -112\n > exponent = base.exponentf( base.float64ToFloat32( -3.14 ) )\n 1\n > exponent = base.exponentf( 0.0 )\n -127\n > exponent = base.exponentf( NaN )\n 128\n\n See Also\n --------\n base.exponent\n","base.factorial":"\nbase.factorial( x )\n Evaluates the factorial of `x`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Factorial.\n\n Examples\n --------\n > var y = base.factorial( 3.0 )\n 6.0\n > y = base.factorial( -1.5 )\n ~-3.545\n > y = base.factorial( -0.5 )\n ~1.772\n > y = base.factorial( 0.5 )\n ~0.886\n > y = base.factorial( -10.0 )\n NaN\n > y = base.factorial( 171.0 )\n Infinity\n > y = base.factorial( NaN )\n NaN\n\n See Also\n --------\n base.factorialln\n","base.factorial2":"\nbase.factorial2( n )\n Evaluates the double factorial of `n`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n n: number\n Input value.\n\n Returns\n -------\n y: number\n Double factorial.\n\n Examples\n --------\n > var y = base.factorial2( 3 )\n 3\n > y = base.factorial2( 5 )\n 15\n > y = base.factorial2( 6 )\n 48\n > y = base.factorial2( 301 )\n Infinity\n > y = base.factorial2( NaN )\n NaN\n\n See Also\n --------\n base.factorial\n","base.factorialln":"\nbase.factorialln( x )\n Evaluates the natural logarithm of the factorial of `x`.\n\n For input values other than negative integers, the function returns\n\n ln( x! ) = ln( Γ(x+1) )\n\n where `Γ` is the Gamma function. For negative integers, the function returns\n `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Natural logarithm of the factorial of `x`.\n\n Examples\n --------\n > var y = base.factorialln( 3.0 )\n ~1.792\n > y = base.factorialln( 2.4 )\n ~1.092\n > y = base.factorialln( -1.0 )\n NaN\n > y = base.factorialln( -1.5 )\n ~1.266\n > y = base.factorialln( NaN )\n NaN\n\n See Also\n --------\n base.factorial\n","base.fallingFactorial":"\nbase.fallingFactorial( x, n )\n Computes the falling factorial of `x` and `n`.\n\n If not provided a nonnegative integer for `n`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n First function parameter.\n\n n: integer\n Second function parameter.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var v = base.fallingFactorial( 0.9, 5 )\n ~0.644\n > v = base.fallingFactorial( -9.0, 3 )\n -990.0\n > v = base.fallingFactorial( 0.0, 2 )\n 0.0\n > v = base.fallingFactorial( 3.0, -2 )\n NaN\n\n See Also\n --------\n base.risingFactorial\n","base.fibonacci":"\nbase.fibonacci( n )\n Computes the nth Fibonacci number.\n\n Fibonacci numbers follow the recurrence relation\n\n F_n = F_{n-1} + F_{n-2}\n\n with seed values F_0 = 0 and F_1 = 1.\n\n If `n` is greater than `78`, the function returns `NaN`, as larger Fibonacci\n numbers cannot be accurately represented due to limitations of double-\n precision floating-point format.\n\n If not provided a nonnegative integer value, the function returns `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Input value.\n\n Returns\n -------\n y: integer\n Fibonacci number.\n\n Examples\n --------\n > var y = base.fibonacci( 0 )\n 0\n > y = base.fibonacci( 1 )\n 1\n > y = base.fibonacci( 2 )\n 1\n > y = base.fibonacci( 3 )\n 2\n > y = base.fibonacci( 4 )\n 3\n > y = base.fibonacci( 79 )\n NaN\n > y = base.fibonacci( NaN )\n NaN\n\n See Also\n --------\n base.binet, base.fibonacciIndex, base.lucas, base.negafibonacci\n","base.fibonacciIndex":"\nbase.fibonacciIndex( F )\n Computes the Fibonacci number index.\n\n If not provided a nonnegative integer value, the function returns `NaN`.\n\n If provided `F <= 1` or `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n F: integer\n Fibonacci number.\n\n Returns\n -------\n n: number\n Fibonacci number index.\n\n Examples\n --------\n > var n = base.fibonacciIndex( 2 )\n 3\n > n = base.fibonacciIndex( 3 )\n 4\n > n = base.fibonacciIndex( 5 )\n 5\n > n = base.fibonacciIndex( NaN )\n NaN\n > n = base.fibonacciIndex( 1 )\n NaN\n\n See Also\n --------\n base.fibonacci\n","base.fibpoly":"\nbase.fibpoly( n, x )\n Evaluates a Fibonacci polynomial.\n\n Parameters\n ----------\n n: integer\n Fibonacci polynomial to evaluate.\n\n x: number\n Value at which to evaluate the Fibonacci polynomial.\n\n Returns\n -------\n out: number\n Evaluated Fibonacci polynomial.\n\n Examples\n --------\n // 2^4 + 3*2^2 + 1\n > var v = base.fibpoly( 5, 2.0 )\n 29.0\n\n\nbase.fibpoly.factory( n )\n Returns a function for evaluating a Fibonacci polynomial.\n\n Parameters\n ----------\n n: integer\n Fibonacci polynomial to evaluate.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a Fibonacci polynomial.\n\n Examples\n --------\n > var polyval = base.fibpoly.factory( 5 );\n\n // 1^4 + 3*1^2 + 1\n > var v = polyval( 1.0 )\n 5.0\n\n // 2^4 + 3*2^2 + 1\n > v = polyval( 2.0 )\n 29.0\n\n See Also\n --------\n base.evalpoly, base.lucaspoly\n","base.fibpoly.factory":"\nbase.fibpoly.factory( n )\n Returns a function for evaluating a Fibonacci polynomial.\n\n Parameters\n ----------\n n: integer\n Fibonacci polynomial to evaluate.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a Fibonacci polynomial.\n\n Examples\n --------\n > var polyval = base.fibpoly.factory( 5 );\n\n // 1^4 + 3*1^2 + 1\n > var v = polyval( 1.0 )\n 5.0\n\n // 2^4 + 3*2^2 + 1\n > v = polyval( 2.0 )\n 29.0\n\n See Also\n --------\n base.evalpoly, base.lucaspoly","base.firstCodePoint":"\nbase.firstCodePoint( str, n )\n Returns the first `n` Unicode code points of a string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n n: integer\n Number of Unicode code points to return.\n\n Returns\n -------\n out: string\n Output string.\n\n Examples\n --------\n > var out = base.firstCodePoint( 'beep', 1 )\n 'b'\n > out = base.firstCodePoint( 'Boop', 1 )\n 'B'\n > out = base.firstCodePoint( 'foo bar', 5 )\n 'foo b'\n\n See Also\n --------\n base.firstCodeUnit, base.firstGraphemeCluster, base.lastCodePoint, base.removeFirstCodePoint, firstChar\n","base.firstCodeUnit":"\nbase.firstCodeUnit( str, n )\n Returns the first `n` UTF-16 code units of a string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n n: integer\n Number of UTF-16 code units to return.\n\n Returns\n -------\n out: string\n Output string.\n\n Examples\n --------\n > var out = base.firstCodeUnit( 'beep', 1 )\n 'b'\n > out = base.firstCodeUnit( 'Boop', 1 )\n 'B'\n > out = base.firstCodeUnit( 'foo bar', 5 )\n 'foo b'\n\n See Also\n --------\n base.firstCodePoint, base.firstGraphemeCluster, base.last, base.removeFirst, firstChar\n","base.firstGraphemeCluster":"\nbase.firstGraphemeCluster( str, n )\n Returns the first `n` grapheme clusters (i.e., user-perceived characters) of\n a string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n n: integer\n Number of grapheme clusters to return.\n\n Returns\n -------\n out: string\n Output string.\n\n Examples\n --------\n > var out = base.firstGraphemeCluster( 'beep', 1 )\n 'b'\n > out = base.firstGraphemeCluster( 'Boop', 1 )\n 'B'\n > out = base.firstGraphemeCluster( 'foo bar', 5 )\n 'foo b'\n\n See Also\n --------\n base.firstCodeUnit, base.firstCodePoint, base.lastGraphemeCluster, base.removeFirstGraphemeCluster, firstChar\n","base.flipsign":"\nbase.flipsign( x, y )\n Returns a double-precision floating-point number with the magnitude of `x`\n and the sign of `x*y`.\n\n The function only returns `-x` when `y` is a negative number.\n\n According to the IEEE 754 standard, a `NaN` has a biased exponent equal to\n `2047`, a significand greater than `0`, and a sign bit equal to either `1`\n or `0`. In which case, `NaN` may not correspond to just one but many binary\n representations. Accordingly, care should be taken to ensure that `y` is not\n `NaN`; otherwise, behavior may be indeterminate.\n\n Parameters\n ----------\n x: number\n Number from which to derive a magnitude.\n\n y: number\n Number from which to derive a sign.\n\n Returns\n -------\n z: number\n Double-precision floating-point number.\n\n Examples\n --------\n > var z = base.flipsign( -3.0, 10.0 )\n -3.0\n > z = base.flipsign( -3.0, -1.0 )\n 3.0\n > z = base.flipsign( 1.0, -0.0 )\n -1.0\n > z = base.flipsign( -3.0, -0.0 )\n 3.0\n > z = base.flipsign( -0.0, 1.0 )\n -0.0\n > z = base.flipsign( 0.0, -1.0 )\n -0.0\n\n See Also\n --------\n base.copysign\n","base.flipsignf":"\nbase.flipsignf( x, y )\n Returns a single-precision floating-point number with the magnitude of `x`\n and the sign of `x*y`.\n\n The function only returns `-x` when `y` is a negative number.\n\n According to the IEEE 754 standard, a `NaN` has a biased exponent equal to\n `255`, a significand greater than `0`, and a sign bit equal to either `1` or\n `0`. In which case, `NaN` may not correspond to just one but many binary\n representations. Accordingly, care should be taken to ensure that `y` is not\n `NaN`; otherwise, behavior may be indeterminate.\n\n Parameters\n ----------\n x: number\n Number from which to derive a magnitude.\n\n y: number\n Number from which to derive a sign.\n\n Returns\n -------\n z: number\n Single-precision floating-point number.\n\n Examples\n --------\n > var z = base.flipsignf( -3.0, 10.0 )\n -3.0\n > z = base.flipsignf( -3.0, -1.0 )\n 3.0\n > z = base.flipsignf( 1.0, -0.0 )\n -1.0\n > z = base.flipsignf( -3.0, -0.0 )\n 3.0\n > z = base.flipsignf( -0.0, 1.0 )\n -0.0\n > z = base.flipsignf( 0.0, -1.0 )\n -0.0\n\n See Also\n --------\n base.copysignf, base.flipsign\n","base.float32ToInt32":"\nbase.float32ToInt32( x )\n Converts a single-precision floating-point number to a signed 32-bit\n integer.\n\n Parameters\n ----------\n x: float\n Single-precision floating-point number.\n\n Returns\n -------\n out: integer\n Signed 32-bit integer.\n\n Examples\n --------\n > var y = base.float32ToInt32( base.float64ToFloat32( 4294967295.0 ) )\n 0\n > y = base.float32ToInt32( base.float64ToFloat32( 3.14 ) )\n 3\n > y = base.float32ToInt32( base.float64ToFloat32( -3.14 ) )\n -3\n > y = base.float32ToInt32( base.float64ToFloat32( NaN ) )\n 0\n > y = base.float32ToInt32( FLOAT32_PINF )\n 0\n > y = base.float32ToInt32( FLOAT32_NINF )\n 0\n\n See Also\n --------\n base.float32ToUint32","base.float32ToUint32":"\nbase.float32ToUint32( x )\n Converts a single-precision floating-point number to a unsigned 32-bit\n integer.\n\n Parameters\n ----------\n x: float\n Single-precision floating-point number.\n\n Returns\n -------\n out: integer\n Unsigned 32-bit integer.\n\n Examples\n --------\n > var y = base.float32ToUint32( base.float64ToFloat32( 4294967297.0 ) )\n 0\n > y = base.float32ToUint32( base.float64ToFloat32( 3.14 ) )\n 3\n > y = base.float32ToUint32( base.float64ToFloat32( -3.14 ) )\n 4294967293\n > y = base.float32ToUint32( base.float64ToFloat32( NaN ) )\n 0\n > y = base.float32ToUint32( FLOAT32_PINF )\n 0\n > y = base.float32ToUint32( FLOAT32_NINF )\n 0\n\n See Also\n --------\n base.float32ToInt32","base.float64ToFloat32":"\nbase.float64ToFloat32( x )\n Converts a double-precision floating-point number to the nearest single-\n precision floating-point number.\n\n Parameters\n ----------\n x: number\n Double-precision floating-point number.\n\n Returns\n -------\n out: float\n Nearest single-precision floating-point number.\n\n Examples\n --------\n > var y = base.float64ToFloat32( 1.337 )\n 1.3370000123977661\n","base.float64ToInt32":"\nbase.float64ToInt32( x )\n Converts a double-precision floating-point number to a signed 32-bit\n integer.\n\n Parameters\n ----------\n x: number\n Double-precision floating-point number.\n\n Returns\n -------\n out: integer\n Signed 32-bit integer.\n\n Examples\n --------\n > var y = base.float64ToInt32( 4294967295.0 )\n -1\n > y = base.float64ToInt32( 3.14 )\n 3\n > y = base.float64ToInt32( -3.14 )\n -3\n > y = base.float64ToInt32( NaN )\n 0\n > y = base.float64ToInt32( PINF )\n 0\n > y = base.float64ToInt32( NINF )\n 0\n\n See Also\n --------\n base.float64ToUint32","base.float64ToInt64Bytes":"\nbase.float64ToInt64Bytes( x )\n Converts an integer-valued double-precision floating-point number to a\n signed 64-bit integer byte array according to host byte order (endianness).\n\n This function assumes that the input value is less than the maximum safe\n double-precision floating-point integer plus one (i.e., `2**53`).\n\n Parameters\n ----------\n x: integer\n Integer-valued double-precision floating-point number.\n\n Returns\n -------\n out: Uint8Array\n Byte array.\n\n Examples\n --------\n > var y = base.float64ToInt64Bytes( 4294967297.0 )\n \n\n\nbase.float64ToInt64Bytes.assign( x, out, stride, offset )\n Converts an integer-valued double-precision floating-point number to a\n signed 64-bit integer byte array according to host byte order (endianness)\n and assigns results to a provided output array.\n\n This function assumes that the input value is less than the maximum safe\n double-precision floating-point integer plus one (i.e., `2**53`).\n\n Parameters\n ----------\n x: integer\n Integer-valued double-precision floating-point number.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array.\n\n Examples\n --------\n > var out = new Uint8Array( 16 );\n > var y = base.float64ToInt64Bytes( 4294967297.0, out, 2, 1 )\n \n\n See Also\n --------\n base.float64ToInt32","base.float64ToInt64Bytes.assign":"\nbase.float64ToInt64Bytes.assign( x, out, stride, offset )\n Converts an integer-valued double-precision floating-point number to a\n signed 64-bit integer byte array according to host byte order (endianness)\n and assigns results to a provided output array.\n\n This function assumes that the input value is less than the maximum safe\n double-precision floating-point integer plus one (i.e., `2**53`).\n\n Parameters\n ----------\n x: integer\n Integer-valued double-precision floating-point number.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array.\n\n Examples\n --------\n > var out = new Uint8Array( 16 );\n > var y = base.float64ToInt64Bytes( 4294967297.0, out, 2, 1 )\n \n\n See Also\n --------\n base.float64ToInt32","base.float64ToUint32":"\nbase.float64ToUint32( x )\n Converts a double-precision floating-point number to a unsigned 32-bit\n integer.\n\n Parameters\n ----------\n x: number\n Double-precision floating-point number.\n\n Returns\n -------\n out: integer\n Unsigned 32-bit integer.\n\n Examples\n --------\n > var y = base.float64ToUint32( 4294967297.0 )\n 1\n > y = base.float64ToUint32( 3.14 )\n 3\n > y = base.float64ToUint32( -3.14 )\n 4294967293\n > y = base.float64ToUint32( NaN )\n 0\n > y = base.float64ToUint32( PINF )\n 0\n > y = base.float64ToUint32( NINF )\n 0\n\n See Also\n --------\n base.float64ToInt32","base.floor":"\nbase.floor( x )\n Rounds a double-precision floating-point number toward negative infinity.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.floor( 3.14 )\n 3.0\n > y = base.floor( -4.2 )\n -5.0\n > y = base.floor( -4.6 )\n -5.0\n > y = base.floor( 9.5 )\n 9.0\n > y = base.floor( -0.0 )\n -0.0\n\n See Also\n --------\n base.ceil, base.round\n","base.floor2":"\nbase.floor2( x )\n Rounds a numeric value to the nearest power of two toward negative infinity.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.floor2( 3.14 )\n 2.0\n > y = base.floor2( -4.2 )\n -8.0\n > y = base.floor2( -4.6 )\n -8.0\n > y = base.floor2( 9.5 )\n 8.0\n > y = base.floor2( 13.0 )\n 8.0\n > y = base.floor2( -13.0 )\n -16.0\n > y = base.floor2( -0.0 )\n -0.0\n\n See Also\n --------\n base.ceil2, base.floor, base.floor10, base.round2\n","base.floor10":"\nbase.floor10( x )\n Rounds a numeric value to the nearest power of ten toward negative infinity.\n\n The function may not return accurate results for subnormals due to a general\n loss in precision.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.floor10( 3.14 )\n 1.0\n > y = base.floor10( -4.2 )\n -10.0\n > y = base.floor10( -4.6 )\n -10.0\n > y = base.floor10( 9.5 )\n 1.0\n > y = base.floor10( 13.0 )\n 10.0\n > y = base.floor10( -13.0 )\n -100.0\n > y = base.floor10( -0.0 )\n -0.0\n\n See Also\n --------\n base.ceil10, base.floor, base.floor2, base.round10\n","base.floorb":"\nbase.floorb( x, n, b )\n Rounds a numeric value to the nearest multiple of `b^n` toward negative\n infinity.\n\n Due to floating-point rounding error, rounding may not be exact.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Integer power.\n\n b: integer\n Base.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n // Round to 4 decimal places:\n > var y = base.floorb( 3.14159, -4, 10 )\n 3.1415\n\n // If `n = 0` or `b = 1`, standard round behavior:\n > y = base.floorb( 3.14159, 0, 2 )\n 3.0\n\n // Round to nearest multiple of two toward negative infinity:\n > y = base.floorb( 5.0, 1, 2 )\n 4.0\n\n See Also\n --------\n base.ceilb, base.floor, base.floorn, base.roundb\n","base.floorf":"\nbase.floorf( x )\n Rounds a single-precision floating-point number toward negative infinity.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.floorf( 3.14 )\n 3.0\n > y = base.floorf( -4.2 )\n -5.0\n > y = base.floorf( -4.6 )\n -5.0\n > y = base.floorf( 9.5 )\n 9.0\n > y = base.floorf( -0.0 )\n -0.0\n\n See Also\n --------\n base.ceilf, base.floor\n","base.floorn":"\nbase.floorn( x, n )\n Rounds a double-precision floating-point number to the nearest multiple of\n `10^n` toward negative infinity.\n\n When operating on floating-point numbers in bases other than `2`, rounding\n to specified digits can be inexact.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Integer power of 10.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n // Round to 4 decimal places:\n > var y = base.floorn( 3.14159, -4 )\n 3.1415\n\n // If `n = 0`, standard round toward negative infinity behavior:\n > y = base.floorn( 3.14159, 0 )\n 3.0\n\n // Round to nearest thousand:\n > y = base.floorn( 12368.0, 3 )\n 12000.0\n\n\n See Also\n --------\n base.ceiln, base.floor, base.floorb, base.roundn\n","base.floorsd":"\nbase.floorsd( x, n, b )\n Rounds a numeric value to the nearest number toward negative infinity with\n `n` significant figures.\n\n Parameters\n ----------\n x: number\n Input value.\n\n n: integer\n Number of significant figures. Must be greater than 0.\n\n b: integer\n Base. Must be greater than 0.\n\n Returns\n -------\n y: number\n Rounded value.\n\n Examples\n --------\n > var y = base.floorsd( 3.14159, 5, 10 )\n 3.1415\n > y = base.floorsd( 3.14159, 1, 10 )\n 3.0\n > y = base.floorsd( 12368.0, 2, 10 )\n 12000.0\n > y = base.floorsd( 0.0313, 2, 2 )\n 0.03125\n\n See Also\n --------\n base.ceilsd, base.floor, base.roundsd, base.truncsd\n","base.forEachChar":"\nbase.forEachChar( str, clbk[, thisArg] )\n Invokes a function for each UTF-16 code unit in a string.\n\n When invoked, the provided function is provided three arguments:\n\n - value: character\n - index: character index\n - str: input string\n\n Parameters\n ----------\n str: string\n Input string over which to iterate.\n\n clbk: Function\n The function to invoke for each UTF-16 code unit in the input string.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n out: string\n Input string.\n\n Examples\n --------\n > var n = 0;\n > function fcn() { n += 1; };\n > base.forEachChar( 'hello world!', fcn );\n > n\n 12\n\n See Also\n --------\n base.forEachCodePoint, base.forEachGraphemeCluster, forEachChar\n","base.forEachCodePoint":"\nbase.forEachCodePoint( str, clbk[, thisArg] )\n Invokes a function for each Unicode code point in a string.\n\n When invoked, the provided function is provided three arguments:\n\n - value: code point\n - index: starting code point index\n - str: input string\n\n Parameters\n ----------\n str: string\n Input string over which to iterate.\n\n clbk: Function\n The function to invoke for each Unicode code point in the input string.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n out: string\n Input string.\n\n Examples\n --------\n > var n = 0;\n > function fcn() { n += 1; };\n > base.forEachCodePoint( 'hello world!', fcn );\n > n\n 12\n\n See Also\n --------\n base.forEachChar, base.forEachGraphemeCluster, forEachChar\n","base.forEachCodePointRight":"\nbase.forEachCodePointRight( str, clbk[, thisArg] )\n Invokes a function for each Unicode code point in a string, iterating from\n right to left.\n\n When invoked, the provided function is provided three arguments:\n\n - value: code point\n - index: starting code point index\n - str: input string\n\n Parameters\n ----------\n str: string\n Input string over which to iterate.\n\n clbk: Function\n The function to invoke for each Unicode code point in the input string.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n out: string\n Input string.\n\n Examples\n --------\n > var n = 0;\n > function fcn() { n += 1; };\n > base.forEachCodePointRight( 'hello world!', fcn );\n > n\n 12\n\n See Also\n --------\n base.forEachCodePoint, base.forEachRight\n","base.forEachGraphemeCluster":"\nbase.forEachGraphemeCluster( str, clbk[, thisArg] )\n Invokes a function for each grapheme cluster (i.e., user-perceived\n character) in a string.\n\n When invoked, the provided function is provided three arguments:\n\n - value: grapheme cluster\n - index: starting grapheme cluster index\n - str: input string\n\n Parameters\n ----------\n str: string\n Input string over which to iterate.\n\n clbk: Function\n The function to invoke for each grapheme cluster in the input string.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n out: string\n Input string.\n\n Examples\n --------\n > var n = 0;\n > function fcn() { n += 1; };\n > base.forEachGraphemeCluster( 'hello world!', fcn );\n > n\n 12\n\n See Also\n --------\n base.forEachChar, base.forEachCodePoint, forEachChar\n","base.forEachRight":"\nbase.forEachRight( str, clbk[, thisArg] )\n Invokes a function for each UTF-16 code unit in a string, iterating from \n right to left.\n\n When invoked, the provided function is provided three arguments:\n\n - value: character\n - index: character index\n - str: input string\n\n Parameters\n ----------\n str: string\n Input string over which to iterate.\n\n clbk: Function\n Function to invoke for each UTF-16 code unit in the input string.\n\n thisArg: any (optional)\n Execution context.\n\n Returns\n -------\n out: string\n Input string.\n\n Examples\n --------\n > var n = 0;\n > function fcn() { n += 1; };\n > base.forEachRight( 'hello world!', fcn );\n > n\n 12\n\n See Also\n --------\n base.forEachChar, base.forEachCodePointRight\n","base.formatInterpolate":"\nbase.formatInterpolate( tokens, ...args )\n Generate string from a token array by interpolating values.\n\n Parameters\n ----------\n tokens: Array\n Array of string parts and format identifier objects.\n\n args: ...any\n Variable values.\n\n Returns\n -------\n out: string\n Formatted string.\n\n Examples\n --------\n > var out = base.formatInterpolate( [ 'beep ', { 'specifier': 's' } ], 'boop' )\n 'beep boop'\n > out = base.formatInterpolate( [ 'baz ', { 'specifier': 'd', 'precision': 2 } ], 1 )\n 'baz 1.00'\n > out = base.formatInterpolate( [ { 'specifier': 'u', 'width': 6 } ], 12 )\n ' 12'\n\n See Also\n --------\n base.formatTokenize\n","base.formatTokenize":"\nbase.formatTokenize( str )\n Tokenize a string into an array of string parts and format identifier\n objects.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: Array\n Array of string parts and format identifier objects.\n\n Examples\n --------\n > var out = base.formatTokenize( 'Hello %s!' )\n [ 'Hello ', {...}, '!' ]\n > out = base.formatTokenize( '%s %s %d' )\n [ {...}, ' ', {...}, ' ', {...}, ' ' ]\n > out = base.formatTokenize( 'Pi: %.2f' )\n [ 'Pi: ', {...} ]\n\n See Also\n --------\n base.formatInterpolate\n","base.fresnel":"\nbase.fresnel( x )\n Computes the Fresnel integrals S(x) and C(x).\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: Array\n S(x) and C(x).\n\n Examples\n --------\n > var y = base.fresnel( 0.0 )\n [ ~0.0, ~0.0 ]\n > y = base.fresnel( 1.0 )\n [ ~0.438, ~0.780 ]\n > y = base.fresnel( PINF )\n [ ~0.5, ~0.5 ]\n > y = base.fresnel( NINF )\n [ ~-0.5, ~-0.5 ]\n > y = base.fresnel( NaN )\n [ NaN, NaN ]\n\n\nbase.fresnel.assign( x, out, stride, offset )\n Computes the Fresnel integrals S(x) and C(x) and assigns results to a\n provided output array.\n\n Parameters\n ----------\n x: number\n Input value.\n\n out: Array\n Destination array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n S(x) and C(x).\n\n Examples\n --------\n > var out = new Float64Array( 2 );\n > var v = base.fresnel.assign( 0.0, out, 1, 0 )\n [ ~0.0, ~0.0 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.fresnelc, base.fresnels","base.fresnel.assign":"\nbase.fresnel.assign( x, out, stride, offset )\n Computes the Fresnel integrals S(x) and C(x) and assigns results to a\n provided output array.\n\n Parameters\n ----------\n x: number\n Input value.\n\n out: Array\n Destination array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n S(x) and C(x).\n\n Examples\n --------\n > var out = new Float64Array( 2 );\n > var v = base.fresnel.assign( 0.0, out, 1, 0 )\n [ ~0.0, ~0.0 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.fresnelc, base.fresnels","base.fresnelc":"\nbase.fresnelc( x )\n Computes the Fresnel integral C(x).\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n C(x).\n\n Examples\n --------\n > var y = base.fresnelc( 0.0 )\n ~0.0\n > y = base.fresnelc( 1.0 )\n ~0.780\n > y = base.fresnelc( PINF )\n ~0.5\n > y = base.fresnelc( NINF )\n ~-0.5\n > y = base.fresnelc( NaN )\n NaN\n\n See Also\n --------\n base.fresnel, base.fresnels\n","base.fresnels":"\nbase.fresnels( x )\n Computes the Fresnel integral S(x).\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n S(x).\n\n Examples\n --------\n > var y = base.fresnels( 0.0 )\n ~0.0\n > y = base.fresnels( 1.0 )\n ~0.438\n > y = base.fresnels( PINF )\n ~0.5\n > y = base.fresnels( NINF )\n ~-0.5\n > y = base.fresnels( NaN )\n NaN\n\n See Also\n --------\n base.fresnel, base.fresnelc\n","base.frexp":"\nbase.frexp( x )\n Splits a double-precision floating-point number into a normalized fraction\n and an integer power of two.\n\n The first element of the returned array is the normalized fraction and the\n second is the exponent. The normalized fraction and exponent satisfy the\n relation\n\n x = frac * 2^exp\n\n If provided positive or negative zero, `NaN`, or positive or negative\n infinity, the function returns a two-element array containing the input\n value and an exponent equal to zero.\n\n For all other numeric input values, the absolute value of the normalized\n fraction resides on the interval [0.5,1).\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n out: Array\n A normalized fraction and an exponent.\n\n Examples\n --------\n > var out = base.frexp( 4.0 )\n [ 0.5, 3 ]\n > out = base.frexp( 0.0 )\n [ 0.0, 0 ]\n > out = base.frexp( -0.0 )\n [ -0.0, 0 ]\n > out = base.frexp( NaN )\n [ NaN, 0 ]\n > out = base.frexp( PINF )\n [ Infinity, 0 ]\n > out = base.frexp( NINF )\n [ -Infinity, 0 ]\n\n\nbase.frexp.assign( x, out, stride, offset )\n Splits a double-precision floating-point number into a normalized fraction\n and an integer power of two and assigns results to a provided output array.\n\n The first element of the returned array is the normalized fraction and the\n second is the exponent. The normalized fraction and exponent satisfy the\n relation\n\n x = frac * 2^exp\n\n If provided positive or negative zero, `NaN`, or positive or negative\n infinity, the function returns a two-element array containing the input\n value and an exponent equal to zero.\n\n For all other numeric input values, the absolute value of the normalized\n fraction resides on the interval [0.5,1).\n\n Parameters\n ----------\n x: number\n Input value.\n\n out: Array\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n A normalized fraction and an exponent.\n\n Examples\n --------\n > var out = new Float64Array( 2 );\n > var y = base.frexp.assign( 4.0, out, 1, 0 )\n [ 0.5, 3 ]\n > var bool = ( y === out )\n true\n\n See Also\n --------\n base.ldexp\n","base.frexp.assign":"\nbase.frexp.assign( x, out, stride, offset )\n Splits a double-precision floating-point number into a normalized fraction\n and an integer power of two and assigns results to a provided output array.\n\n The first element of the returned array is the normalized fraction and the\n second is the exponent. The normalized fraction and exponent satisfy the\n relation\n\n x = frac * 2^exp\n\n If provided positive or negative zero, `NaN`, or positive or negative\n infinity, the function returns a two-element array containing the input\n value and an exponent equal to zero.\n\n For all other numeric input values, the absolute value of the normalized\n fraction resides on the interval [0.5,1).\n\n Parameters\n ----------\n x: number\n Input value.\n\n out: Array\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array\n A normalized fraction and an exponent.\n\n Examples\n --------\n > var out = new Float64Array( 2 );\n > var y = base.frexp.assign( 4.0, out, 1, 0 )\n [ 0.5, 3 ]\n > var bool = ( y === out )\n true\n\n See Also\n --------\n base.ldexp","base.fromBinaryString":"\nbase.fromBinaryString( bstr )\n Creates a double-precision floating-point number from a literal bit\n representation.\n\n Parameters\n ----------\n bstr: string\n Literal bit representation.\n\n Returns\n -------\n out: number\n Double-precision floating-point number.\n\n Examples\n --------\n > var bstr;\n > bstr = '0100000000010000000000000000000000000000000000000000000000000000';\n > var val = base.fromBinaryString( bstr )\n 4.0\n > bstr = '0100000000001001001000011111101101010100010001000010110100011000';\n > val = base.fromBinaryString( bstr )\n 3.141592653589793\n > bstr = '1111111111100001110011001111001110000101111010111100100010100000';\n > val = base.fromBinaryString( bstr )\n -1.0e308\n\n // The function handles subnormals:\n > bstr = '1000000000000000000000000000000000000000000000000001100011010011';\n > val = base.fromBinaryString( bstr )\n -3.14e-320\n > bstr = '0000000000000000000000000000000000000000000000000000000000000001';\n > val = base.fromBinaryString( bstr )\n 5.0e-324\n\n // The function handles special values:\n > bstr = '0000000000000000000000000000000000000000000000000000000000000000';\n > val = base.fromBinaryString( bstr )\n 0.0\n > bstr = '1000000000000000000000000000000000000000000000000000000000000000';\n > val = base.fromBinaryString( bstr )\n -0.0\n > bstr = '0111111111111000000000000000000000000000000000000000000000000000';\n > val = base.fromBinaryString( bstr )\n NaN\n > bstr = '0111111111110000000000000000000000000000000000000000000000000000';\n > val = base.fromBinaryString( bstr )\n Infinity\n > bstr = '1111111111110000000000000000000000000000000000000000000000000000';\n > val = base.fromBinaryString( bstr )\n -Infinity\n\n See Also\n --------\n base.fromBinaryStringf, base.toBinaryString\n","base.fromBinaryStringf":"\nbase.fromBinaryStringf( bstr )\n Creates a single-precision floating-point number from an IEEE 754 literal\n bit representation.\n\n Parameters\n ----------\n bstr: string\n Literal bit representation.\n\n Returns\n -------\n out: float\n Single-precision floating-point number.\n\n Examples\n --------\n > var bstr = '01000000100000000000000000000000';\n > var val = base.fromBinaryStringf( bstr )\n 4.0\n > bstr = '01000000010010010000111111011011';\n > val = base.fromBinaryStringf( bstr )\n ~3.14\n > bstr = '11111111011011000011101000110011';\n > val = base.fromBinaryStringf( bstr )\n ~-3.14e+38\n\n // The function handles subnormals:\n > bstr = '10000000000000000000000000010110';\n > val = base.fromBinaryStringf( bstr )\n ~-3.08e-44\n > bstr = '00000000000000000000000000000001';\n > val = base.fromBinaryStringf( bstr )\n ~1.40e-45\n\n // The function handles special values:\n > bstr = '00000000000000000000000000000000';\n > val = base.fromBinaryStringf( bstr )\n 0.0\n > bstr = '10000000000000000000000000000000';\n > val = base.fromBinaryStringf( bstr )\n -0.0\n > bstr = '01111111110000000000000000000000';\n > val = base.fromBinaryStringf( bstr )\n NaN\n > bstr = '01111111100000000000000000000000';\n > val = base.fromBinaryStringf( bstr )\n Infinity\n > bstr = '11111111100000000000000000000000';\n > val = base.fromBinaryStringf( bstr )\n -Infinity\n\n See Also\n --------\n base.toBinaryStringf, base.fromBinaryString\n","base.fromBinaryStringUint8":"\nbase.fromBinaryStringUint8( bstr )\n Creates an unsigned 8-bit integer from a literal bit representation.\n\n Parameters\n ----------\n bstr: string\n Literal bit representation.\n\n Returns\n -------\n out: integer\n Unsigned 8-bit integer.\n\n Examples\n --------\n > var bstr = '01010101';\n > var val = base.fromBinaryStringUint8( bstr )\n 85\n > bstr = '00000000';\n > val = base.fromBinaryStringUint8( bstr )\n 0\n > bstr = '00000010';\n > val = base.fromBinaryStringUint8( bstr )\n 2\n > bstr = '11111111';\n > val = base.fromBinaryStringUint8( bstr )\n 255\n\n See Also\n --------\n base.fromBinaryStringUint16, base.fromBinaryStringUint32, base.toBinaryStringUint8\n","base.fromBinaryStringUint16":"\nbase.fromBinaryStringUint16( bstr )\n Creates an unsigned 16-bit integer from a literal bit representation.\n\n Parameters\n ----------\n bstr: string\n Literal bit representation.\n\n Returns\n -------\n out: integer\n Unsigned 16-bit integer.\n\n Examples\n --------\n > var bstr = '0101010101010101';\n > var val = base.fromBinaryStringUint16( bstr )\n 21845\n > bstr = '0000000000000000';\n > val = base.fromBinaryStringUint16( bstr )\n 0\n > bstr = '0000000000000010';\n > val = base.fromBinaryStringUint16( bstr )\n 2\n > bstr = '1111111111111111';\n > val = base.fromBinaryStringUint16( bstr )\n 65535\n\n See Also\n --------\n base.toBinaryStringUint16, base.fromBinaryStringUint32, base.fromBinaryStringUint8\n","base.fromBinaryStringUint32":"\nbase.fromBinaryStringUint32( bstr )\n Creates an unsigned 32-bit integer from a literal bit representation.\n\n Parameters\n ----------\n bstr: string\n Literal bit representation.\n\n Returns\n -------\n out: integer\n Unsigned 32-bit integer.\n\n Examples\n --------\n > var bstr = '01010101010101010101010101010101';\n > var val = base.fromBinaryStringUint32( bstr )\n 1431655765\n > bstr = '00000000000000000000000000000000';\n > val = base.fromBinaryStringUint32( bstr )\n 0\n > bstr = '00000000000000000000000000000010';\n > val = base.fromBinaryStringUint32( bstr )\n 2\n > bstr = '11111111111111111111111111111111';\n > val = base.fromBinaryStringUint32( bstr )\n 4294967295\n\n See Also\n --------\n base.fromBinaryStringUint16, base.toBinaryStringUint32, base.fromBinaryStringUint8\n","base.fromInt64Bytes":"\nbase.fromInt64Bytes( bytes, stride, offset )\n Converts a signed 64-bit integer byte array to a double-precision floating-\n point number.\n\n The function assumes host byte order (endianness).\n\n Parameters\n ----------\n bytes: Array|TypedArray|Object\n Byte array.\n\n stride: integer\n Index stride.\n\n offset: integer\n Index offset.\n\n Returns\n -------\n out: number\n Number.\n\n Examples\n --------\n > var bytes = new Uint8Array( [ 255, 255, 255, 255, 255, 255, 255, 255 ] );\n > var y = base.fromInt64Bytes( bytes, 1, 0 )\n -1.0\n\n See Also\n --------\n base.float64ToInt64Bytes","base.fromWordf":"\nbase.fromWordf( word )\n Creates a single-precision floating-point number from an unsigned integer\n corresponding to an IEEE 754 binary representation.\n\n Parameters\n ----------\n word: integer\n Unsigned integer.\n\n Returns\n -------\n out: float\n Single-precision floating-point number.\n\n Examples\n --------\n > var word = 1068180177; // => 0 01111111 01010110010001011010001\n > var f32 = base.fromWordf( word ) // when printed, promoted to float64\n 1.3370000123977661\n\n See Also\n --------\n base.fromWords\n","base.fromWords":"\nbase.fromWords( high, low )\n Creates a double-precision floating-point number from a higher order word\n (unsigned 32-bit integer) and a lower order word (unsigned 32-bit integer).\n\n Parameters\n ----------\n high: integer\n Higher order word (unsigned 32-bit integer).\n\n low: integer\n Lower order word (unsigned 32-bit integer).\n\n Returns\n -------\n out: number\n Double-precision floating-point number.\n\n Examples\n --------\n > var v = base.fromWords( 1774486211, 2479577218 )\n 3.14e201\n > v = base.fromWords( 3221823995, 1413754136 )\n -3.141592653589793\n > v = base.fromWords( 0, 0 )\n 0.0\n > v = base.fromWords( 2147483648, 0 )\n -0.0\n > v = base.fromWords( 2146959360, 0 )\n NaN\n > v = base.fromWords( 2146435072, 0 )\n Infinity\n > v = base.fromWords( 4293918720, 0 )\n -Infinity\n\n See Also\n --------\n base.fromWordf\n","base.gamma":"\nbase.gamma( x )\n Evaluates the gamma function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.gamma( 4.0 )\n 6.0\n > y = base.gamma( -1.5 )\n ~2.363\n > y = base.gamma( -0.5 )\n ~-3.545\n > y = base.gamma( 0.5 )\n ~1.772\n > y = base.gamma( 0.0 )\n Infinity\n > y = base.gamma( -0.0 )\n -Infinity\n > y = base.gamma( NaN )\n NaN\n\n See Also\n --------\n base.gamma1pm1, base.gammainc, base.gammaincinv, base.gammaln\n","base.gamma1pm1":"\nbase.gamma1pm1( x )\n Computes `gamma(x+1) - 1` without cancellation errors, where `gamma(x)` is\n the gamma function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.gamma1pm1( 0.2 )\n ~-0.082\n > y = base.gamma1pm1( -6.7 )\n ~-0.991\n > y = base.gamma1pm1( 0.0 )\n 0.0\n > y = base.gamma1pm1( NaN )\n NaN\n\n See Also\n --------\n base.gamma, base.gammainc, base.gammaincinv, base.gammaln\n","base.gammaDeltaRatio":"\nbase.gammaDeltaRatio( z, delta )\n Computes the ratio of two gamma functions.\n\n The ratio is defined as: Γ(z) / Γ(z+Δ).\n\n Parameters\n ----------\n z: number\n First gamma parameter.\n\n delta: number\n Difference.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.gammaDeltaRatio( 2.0, 3.0 )\n ~0.042\n > y = base.gammaDeltaRatio( 4.0, 0.5 )\n ~0.516\n > y = base.gammaDeltaRatio( 100.0, 0.0 )\n 1.0\n > y = base.gammaDeltaRatio( NaN, 3.0 )\n NaN\n > y = base.gammaDeltaRatio( 5.0, NaN )\n NaN\n > y = base.gammaDeltaRatio( NaN, NaN )\n NaN\n\n See Also\n --------\n base.gamma\n","base.gammainc":"\nbase.gammainc( x, s[, regularized[, upper]] )\n Computes the regularized incomplete gamma function.\n\n The `regularized` and `upper` parameters specify whether to evaluate the\n non-regularized and/or upper incomplete gamma functions, respectively.\n\n If provided `x < 0` or `s <= 0`, the function returns `NaN`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n First function parameter.\n\n s: number\n Second function parameter.\n\n regularized: boolean (optional)\n Boolean indicating whether the function should evaluate the regularized\n or non-regularized incomplete gamma function. Default: `true`.\n\n upper: boolean (optional)\n Boolean indicating whether the function should return the upper tail of\n the incomplete gamma function. Default: `false`.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.gammainc( 6.0, 2.0 )\n ~0.9826\n > y = base.gammainc( 1.0, 2.0, true, true )\n ~0.7358\n > y = base.gammainc( 7.0, 5.0 )\n ~0.8270\n > y = base.gammainc( 7.0, 5.0, false )\n ~19.8482\n > y = base.gammainc( NaN, 2.0 )\n NaN\n > y = base.gammainc( 6.0, NaN )\n NaN\n\n See Also\n --------\n base.gamma, base.gamma1pm1, base.gammaincinv, base.gammaln\n","base.gammaincinv":"\nbase.gammaincinv( p, a[, upper] )\n Computes the inverse of the lower incomplete gamma function.\n\n In contrast to a more commonly used definition, the first argument is the\n probability `p` and the second argument is the scale factor `a`.\n\n By default, the function inverts the lower regularized incomplete gamma\n function, `P(x,a)`. To invert the upper function `Q(x,a)`, set the `upper`\n argument to `true`.\n\n If provided `NaN` as any argument, the function returns `NaN`.\n\n If provided `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Probability.\n\n a: number\n Scale parameter.\n\n upper: boolean (optional)\n Boolean indicating if the function should invert the upper tail of the\n incomplete gamma function; i.e., compute `xr` such that `Q(a,xr) = p`.\n Default: `false`.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.gammaincinv( 0.5, 2.0 )\n ~1.678\n > y = base.gammaincinv( 0.1, 10.0 )\n ~6.221\n > y = base.gammaincinv( 0.75, 3.0 )\n ~3.92\n > y = base.gammaincinv( 0.75, 3.0, true )\n ~1.727\n > y = base.gammaincinv( 0.75, NaN )\n NaN\n > y = base.gammaincinv( NaN, 3.0 )\n NaN\n\n See Also\n --------\n base.gamma, base.gamma1pm1, base.gammainc, base.gammaln\n","base.gammaLanczosSum":"\nbase.gammaLanczosSum( x )\n Calculates the Lanczos sum for the approximation of the gamma function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Lanczos sum.\n\n Examples\n --------\n > var y = base.gammaLanczosSum( 4.0 )\n ~950.366\n > y = base.gammaLanczosSum( -1.5 )\n ~1373366.245\n > y = base.gammaLanczosSum( -0.5 )\n ~-699841.735\n > y = base.gammaLanczosSum( 0.5 )\n ~96074.186\n > y = base.gammaLanczosSum( 0.0 )\n Infinity\n > y = base.gammaLanczosSum( NaN )\n NaN\n\n See Also\n --------\n base.gamma, base.gammaLanczosSumExpGScaled\n","base.gammaLanczosSumExpGScaled":"\nbase.gammaLanczosSumExpGScaled( x )\n Calculates the scaled Lanczos sum for the approximation of the gamma\n function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Scaled Lanczos sum.\n\n Examples\n --------\n > var y = base.gammaLanczosSumExpGScaled( 4.0 )\n ~0.018\n > y = base.gammaLanczosSumExpGScaled( -1.5 )\n ~25.337\n > y = base.gammaLanczosSumExpGScaled( -0.5 )\n ~-12.911\n > y = base.gammaLanczosSumExpGScaled( 0.5 )\n ~1.772\n > y = base.gammaLanczosSumExpGScaled( 0.0 )\n Infinity\n > y = base.gammaLanczosSumExpGScaled( NaN )\n NaN\n\n See Also\n --------\n base.gamma, base.gammaLanczosSum\n","base.gammaln":"\nbase.gammaln( x )\n Evaluates the natural logarithm of the gamma function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Natural logarithm of the gamma function.\n\n Examples\n --------\n > var y = base.gammaln( 1.0 )\n 0.0\n > y = base.gammaln( 2.0 )\n 0.0\n > y = base.gammaln( 4.0 )\n ~1.792\n > y = base.gammaln( -0.5 )\n ~1.266\n > y = base.gammaln( 0.5 )\n ~0.572\n > y = base.gammaln( 0.0 )\n Infinity\n > y = base.gammaln( NaN )\n NaN\n\n See Also\n --------\n base.gamma, base.gammainc, base.gammaincinv\n","base.gammasgn":"\nbase.gammasgn( x )\n Computes the sign of the gamma function.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Sign of the gamma function.\n\n Examples\n --------\n > var y = base.gammasgn( 1.0 )\n 1.0\n > y = base.gammasgn( -2.5 )\n -1.0\n > y = base.gammasgn( 0.0 )\n 0.0\n > y = base.gammasgn( NaN )\n NaN\n\n See Also\n --------\n base.gamma","base.gcd":"\nbase.gcd( a, b )\n Computes the greatest common divisor (gcd).\n\n If both `a` and `b` are `0`, the function returns `0`.\n\n Both `a` and `b` must have integer values; otherwise, the function returns\n `NaN`.\n\n Parameters\n ----------\n a: integer\n First integer.\n\n b: integer\n Second integer.\n\n Returns\n -------\n out: integer\n Greatest common divisor.\n\n Examples\n --------\n > var v = base.gcd( 48, 18 )\n 6\n\n See Also\n --------\n base.lcm\n","base.getHighWord":"\nbase.getHighWord( x )\n Returns an unsigned 32-bit integer corresponding to the more significant 32\n bits of a double-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n out: integer\n Higher order word (unsigned 32-bit integer).\n\n Examples\n --------\n > var w = base.getHighWord( 3.14e201 )\n 1774486211\n\n See Also\n --------\n base.getLowWord, base.setHighWord\n","base.getLowWord":"\nbase.getLowWord( x )\n Returns an unsigned 32-bit integer corresponding to the less significant 32\n bits of a double-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n out: integer\n Lower order word (unsigned 32-bit integer).\n\n Examples\n --------\n > var w = base.getLowWord( 3.14e201 )\n 2479577218\n\n See Also\n --------\n base.getHighWord, base.setHighWord\n","base.hacovercos":"\nbase.hacovercos( x )\n Computes the half-value coversed cosine.\n\n The half-value coversed cosine is defined as `(1 + sin(x)) / 2`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Half-value coversed cosine.\n\n Examples\n --------\n > var y = base.hacovercos( 3.14 )\n ~0.5008\n > y = base.hacovercos( -4.2 )\n ~0.9358\n > y = base.hacovercos( -4.6 )\n ~0.9968\n > y = base.hacovercos( 9.5 )\n ~0.4624\n > y = base.hacovercos( -0.0 )\n 0.5\n\n See Also\n --------\n base.hacoversin, base.havercos\n","base.hacoversin":"\nbase.hacoversin( x )\n Computes the half-value coversed sine.\n\n The half-value coversed sine is defined as `(1 - sin(x)) / 2`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Half-value coversed sine.\n\n Examples\n --------\n > var y = base.hacoversin( 3.14 )\n ~0.4992\n > y = base.hacoversin( -4.2 )\n ~0.0642\n > y = base.hacoversin( -4.6 )\n ~0.0032\n > y = base.hacoversin( 9.5 )\n ~0.538\n > y = base.hacoversin( -0.0 )\n 0.5\n\n See Also\n --------\n base.hacovercos, base.haversin\n","base.havercos":"\nbase.havercos( x )\n Computes the half-value versed cosine.\n\n The half-value versed cosine is defined as `(1 + cos(x)) / 2`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Half-value versed cosine.\n\n Examples\n --------\n > var y = base.havercos( 3.14 )\n ~0.0\n > y = base.havercos( -4.2 )\n ~0.2549\n > y = base.havercos( -4.6 )\n ~0.4439\n > y = base.havercos( 9.5 )\n ~0.0014\n > y = base.havercos( -0.0 )\n 1.0\n\n See Also\n --------\n base.haversin, base.vercos\n","base.haversin":"\nbase.haversin( x )\n Computes the half-value versed sine.\n\n The half-value versed sine is defined as `(1 - cos(x)) / 2`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n Returns\n -------\n y: number\n Half-value versed sine.\n\n Examples\n --------\n > var y = base.haversin( 3.14 )\n ~1.0\n > y = base.haversin( -4.2 )\n ~0.7451\n > y = base.haversin( -4.6 )\n ~0.5561\n > y = base.haversin( 9.5 )\n ~0.9986\n > y = base.haversin( -0.0 )\n 0.0\n\n See Also\n --------\n base.havercos, base.versin\n","base.headercase":"\nbase.headercase( str )\n Converts a string to HTTP header case.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n HTTP header-cased string.\n\n Examples\n --------\n > var out = base.headercase( 'Hello World!' )\n 'Hello-World'\n > out = base.headercase( 'beep boop' )\n 'Beep-Boop'\n\n See Also\n --------\n base.camelcase, base.pascalcase, base.uppercase","base.heaviside":"\nbase.heaviside( x[, continuity] )\n Evaluates the Heaviside function.\n\n The `continuity` parameter may be one of the following:\n\n - 'half-maximum': if `x == 0`, the function returns `0.5`.\n - 'left-continuous': if `x == 0`, the function returns `0`.\n - 'right-continuous': if `x == 0`, the function returns `1`.\n\n By default, if `x == 0`, the function returns `NaN` (i.e., the function is\n discontinuous).\n\n Parameters\n ----------\n x: number\n Input value.\n\n continuity: string (optional)\n Specifies how to handle `x == 0`. By default, if `x == 0`, the function\n returns `NaN`.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.heaviside( 3.14 )\n 1.0\n > y = base.heaviside( -3.14 )\n 0.0\n > y = base.heaviside( 0.0 )\n NaN\n > y = base.heaviside( 0.0, 'half-maximum' )\n 0.5\n > y = base.heaviside( 0.0, 'left-continuous' )\n 0.0\n > y = base.heaviside( 0.0, 'right-continuous' )\n 1.0\n\n See Also\n --------\n base.ramp\n","base.hermitepoly":"\nbase.hermitepoly( n, x )\n Evaluates a physicist's Hermite polynomial.\n\n Parameters\n ----------\n n: integer\n Nonnegative polynomial degree.\n\n x: number\n Value at which to evaluate the polynomial.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.hermitepoly( 1, 0.5 )\n 1.0\n > y = base.hermitepoly( -1, 0.5 )\n NaN\n > y = base.hermitepoly( 0, 0.5 )\n 1.0\n > y = base.hermitepoly( 2, 0.5 )\n -1.0\n\n\nbase.hermitepoly.factory( n )\n Returns a function for evaluating a physicist's Hermite polynomial.\n\n Parameters\n ----------\n n: integer\n Nonnegative polynomial degree.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a physicist's Hermite polynomial.\n\n Examples\n --------\n > var polyval = base.hermitepoly.factory( 2 );\n > var v = polyval( 0.5 )\n -1.0\n\n See Also\n --------\n base.evalpoly, base.normhermitepoly\n","base.hermitepoly.factory":"\nbase.hermitepoly.factory( n )\n Returns a function for evaluating a physicist's Hermite polynomial.\n\n Parameters\n ----------\n n: integer\n Nonnegative polynomial degree.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a physicist's Hermite polynomial.\n\n Examples\n --------\n > var polyval = base.hermitepoly.factory( 2 );\n > var v = polyval( 0.5 )\n -1.0\n\n See Also\n --------\n base.evalpoly, base.normhermitepoly","base.hypot":"\nbase.hypot( x, y )\n Computes the hypotenuse avoiding overflow and underflow.\n\n If either argument is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: number\n Hypotenuse.\n\n Examples\n --------\n > var h = base.hypot( -5.0, 12.0 )\n 13.0\n > h = base.hypot( NaN, 12.0 )\n NaN\n > h = base.hypot( -0.0, -0.0 )\n 0.0\n\n","base.hypotf":"\nbase.hypotf( x, y )\n Computes the hypotenuse avoiding overflow and underflow (single-precision).\n\n If either argument is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: number\n Hypotenuse.\n\n Examples\n --------\n > var h = base.hypotf( -5.0, 12.0 )\n 13.0\n > h = base.hypotf( NaN, 12.0 )\n NaN\n > h = base.hypotf( -0.0, -0.0 )\n 0.0\n\n See Also\n --------\n base.hypot\n","base.identity":"\nbase.identity( x )\n Evaluates the identity function for a double-precision floating-point number\n `x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Input value.\n\n Examples\n --------\n > var y = base.identity( -1.0 )\n -1.0\n > y = base.identity( 2.0 )\n 2.0\n > y = base.identity( 0.0 )\n 0.0\n > y = base.identity( -0.0 )\n -0.0\n > y = base.identity( NaN )\n NaN\n\n","base.identityf":"\nbase.identityf( x )\n Evaluates the identity function for a single-precision floating-point number\n `x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Input value.\n\n Examples\n --------\n > var y = base.identityf( -1.0 )\n -1.0\n > y = base.identityf( 2.0 )\n 2.0\n > y = base.identityf( 0.0 )\n 0.0\n > y = base.identityf( -0.0 )\n -0.0\n > y = base.identityf( NaN )\n NaN\n\n See Also\n --------\n base.identityf\n","base.imul":"\nbase.imul( a, b )\n Performs C-like multiplication of two signed 32-bit integers.\n\n Parameters\n ----------\n a: integer\n Signed 32-bit integer.\n\n b: integer\n Signed 32-bit integer.\n\n Returns\n -------\n out: integer\n Product.\n\n Examples\n --------\n > var v = base.imul( -10|0, 4|0 )\n -40\n\n See Also\n --------\n base.imuldw\n","base.imuldw":"\nbase.imuldw( a, b )\n Multiplies two signed 32-bit integers and returns an array of two signed 32-\n bit integers which represents the signed 64-bit integer product.\n\n When computing the product of 32-bit integer values in double-precision\n floating-point format (the default JavaScript numeric data type), computing\n the double word product is necessary in order to avoid exceeding the maximum\n safe double-precision floating-point integer value.\n\n Parameters\n ----------\n a: integer\n Signed 32-bit integer.\n\n b: integer\n Signed 32-bit integer.\n\n Returns\n -------\n out: Array\n Double word product (in big endian order; i.e., the first element\n corresponds to the most significant bits and the second element to the\n least significant bits).\n\n Examples\n --------\n > var v = base.imuldw( 1, 10 )\n [ 0, 10 ]\n\n\nbase.imuldw.assign( a, b, out, stride, offset )\n Multiplies two signed 32-bit integers and assigns results representing the\n signed 64-bit integer product to a provided output array.\n\n When computing the product of 32-bit integer values in double-precision\n floating-point format (the default JavaScript numeric data type), computing\n the double word product is necessary in order to avoid exceeding the maximum\n safe double-precision floating-point integer value.\n\n Parameters\n ----------\n a: integer\n Signed 32-bit integer.\n\n b: integer\n Signed 32-bit integer.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Double word product (in big endian order; i.e., the first element\n corresponds to the most significant bits and the second element to the\n least significant bits).\n\n Examples\n --------\n > var out = [ 0, 0 ];\n > var v = base.imuldw.assign( 1, 10, out, 1, 0 )\n [ 0, 10 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.imul","base.imuldw.assign":"\nbase.imuldw.assign( a, b, out, stride, offset )\n Multiplies two signed 32-bit integers and assigns results representing the\n signed 64-bit integer product to a provided output array.\n\n When computing the product of 32-bit integer values in double-precision\n floating-point format (the default JavaScript numeric data type), computing\n the double word product is necessary in order to avoid exceeding the maximum\n safe double-precision floating-point integer value.\n\n Parameters\n ----------\n a: integer\n Signed 32-bit integer.\n\n b: integer\n Signed 32-bit integer.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Double word product (in big endian order; i.e., the first element\n corresponds to the most significant bits and the second element to the\n least significant bits).\n\n Examples\n --------\n > var out = [ 0, 0 ];\n > var v = base.imuldw.assign( 1, 10, out, 1, 0 )\n [ 0, 10 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.imul","base.int2slice":"\nbase.int2slice( value, max, strict )\n Converts an integer to a Slice object.\n\n In strict mode, the function returns an error object if an input value\n exceeds index bounds.\n\n A returned error object is a plain object having the following properties:\n\n - code: error code.\n\n A returned error object may have one of the following error codes:\n\n - ERR_SLICE_OUT_OF_BOUNDS: a slice exceeds index bounds.\n\n Parameters\n ----------\n value: integer\n Input value.\n\n max: integer\n Index upper bound (exclusive).\n\n strict: boolean\n Boolean indicating whether to enforce strict bounds checking.\n\n Returns\n -------\n s: Slice|Object\n Slice instance (or an error object).\n\n Examples\n --------\n > var s = base.int2slice( -1, 5, false );\n > s.start\n 4\n > s.stop\n 5\n > s.step\n 1\n\n See Also\n --------\n base.seq2slice, base.str2slice\n","base.int32ToUint32":"\nbase.int32ToUint32( x )\n Converts a signed 32-bit integer to an unsigned 32-bit integer.\n\n Parameters\n ----------\n x: integer\n Signed 32-bit integer.\n\n Returns\n -------\n out: integer\n Unsigned 32-bit integer.\n\n Examples\n --------\n > var y = base.int32ToUint32( base.float64ToInt32( -32 ) )\n 4294967264\n > y = base.int32ToUint32( base.float64ToInt32( 3 ) )\n 3\n\n See Also\n --------\n base.uint32ToInt32\n","base.inv":"\nbase.inv( x )\n Computes the multiplicative inverse of a double-precision floating-point\n number `x`.\n\n The multiplicative inverse is defined as `1/x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Multiplicative inverse.\n\n Examples\n --------\n > var y = base.inv( -1.0 )\n -1.0\n > y = base.inv( 2.0 )\n 0.5\n > y = base.inv( 0.0 )\n Infinity\n > y = base.inv( -0.0 )\n -Infinity\n > y = base.inv( NaN )\n NaN\n\n See Also\n --------\n base.pow\n","base.invcase":"\nbase.invcase( str )\n Converts a string to inverse case.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Inverse-cased string.\n\n Examples\n --------\n > var out = base.invcase( 'Hello World!' )\n 'hELLO wORLD!'\n > out = base.invcase( 'I am A tiny LITTLE teapot' )\n 'i AM a TINY little TEAPOT'\n\n See Also\n --------\n base.lowercase, base.uppercase","base.invf":"\nbase.invf( x )\n Computes the multiplicative inverse of a single-precision floating-point\n number `x`.\n\n The multiplicative inverse is defined as `1/x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Multiplicative inverse.\n\n Examples\n --------\n > var y = base.invf( -1.0 )\n -1.0\n > y = base.invf( 2.0 )\n 0.5\n > y = base.invf( 0.0 )\n Infinity\n > y = base.invf( -0.0 )\n -Infinity\n > y = base.invf( NaN )\n NaN\n\n See Also\n --------\n base.inv\n","base.isComposite":"\nbase.isComposite( x )\n Tests if a number is composite.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a composite number.\n\n Examples\n --------\n > var bool = base.isComposite( 10.0 )\n true\n > bool = base.isComposite( 11.0 )\n false\n\n See Also\n --------\n base.isInteger, base.isPrime\n","base.isCoprime":"\nbase.isCoprime( a, b )\n Tests if two numbers are coprime.\n\n Parameters\n ----------\n a: number\n First value.\n\n b: number\n Second value.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the values are coprime.\n\n Examples\n --------\n > var bool = base.isCoprime( 14.0, 15.0 )\n true\n > bool = base.isCoprime( 14.0, 21.0 )\n false\n\n See Also\n --------\n base.isComposite, base.isPrime, base.gcd\n","base.isEven":"\nbase.isEven( x )\n Tests if a finite numeric value is an even number.\n\n The function assumes a finite number. If provided positive or negative\n infinity, the function will return `true`, when, in fact, the result is\n undefined.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is an even number.\n\n Examples\n --------\n > var bool = base.isEven( 5.0 )\n false\n > bool = base.isEven( -2.0 )\n true\n > bool = base.isEven( 0.0 )\n true\n > bool = base.isEven( NaN )\n false\n\n See Also\n --------\n base.isOdd\n","base.isEvenInt32":"\nbase.isEvenInt32( x )\n Tests if a 32-bit integer is even.\n\n Parameters\n ----------\n x: integer\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is an even number.\n\n Examples\n --------\n > var bool = base.isEvenInt32( 5 )\n false\n > bool = base.isEvenInt32( -2 )\n true\n > bool = base.isEvenInt32( 0 )\n true\n\n See Also\n --------\n base.isEven, base.isOddInt32\n","base.isFinite":"\nbase.isFinite( x )\n Tests if a double-precision floating-point numeric value is finite.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is finite.\n\n Examples\n --------\n > var bool = base.isFinite( 5.0 )\n true\n > bool = base.isFinite( -2.0e64 )\n true\n > bool = base.isFinite( PINF )\n false\n > bool = base.isFinite( NINF )\n false\n\n See Also\n --------\n base.isInfinite\n","base.isFinitef":"\nbase.isFinitef( x )\n Tests if a single-precision floating-point numeric value is finite.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is finite.\n\n Examples\n --------\n > var bool = base.isFinitef( 5.0 )\n true\n > bool = base.isFinitef( -1.0e38 )\n true\n > bool = base.isFinitef( FLOAT32_PINF )\n false\n > bool = base.isFinitef( FLOAT32_NINF )\n false\n\n See Also\n --------\n base.isInfinitef\n","base.isInfinite":"\nbase.isInfinite( x )\n Tests if a double-precision floating-point numeric value is infinite.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is infinite.\n\n Examples\n --------\n > var bool = base.isInfinite( PINF )\n true\n > bool = base.isInfinite( NINF )\n true\n > bool = base.isInfinite( 5.0 )\n false\n > bool = base.isInfinite( NaN )\n false\n\n See Also\n --------\n base.isFinite\n","base.isInfinitef":"\nbase.isInfinitef( x )\n Tests if a single-precision floating-point numeric value is infinite.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is infinite.\n\n Examples\n --------\n > var bool = base.isInfinitef( FLOAT32_PINF )\n true\n > bool = base.isInfinitef( FLOAT32_NINF )\n true\n > bool = base.isInfinitef( 5.0 )\n false\n > bool = base.isInfinitef( NaN )\n false\n\n See Also\n --------\n base.isFinitef\n","base.isInteger":"\nbase.isInteger( x )\n Tests if a finite double-precision floating-point number is an integer.\n\n The function assumes a finite number. If provided positive or negative\n infinity, the function will return `true`, when, in fact, the result is\n undefined.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is an integer.\n\n Examples\n --------\n > var bool = base.isInteger( 1.0 )\n true\n > bool = base.isInteger( 3.14 )\n false\n\n","base.isnan":"\nbase.isnan( x )\n Tests if a double-precision floating-point numeric value is `NaN`.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is `NaN`.\n\n Examples\n --------\n > var bool = base.isnan( NaN )\n true\n > bool = base.isnan( 7.0 )\n false\n\n See Also\n --------\n base.isnanf\n","base.isnanf":"\nbase.isnanf( x )\n Tests if a single-precision floating-point numeric value is `NaN`.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is `NaN`.\n\n Examples\n --------\n > var bool = base.isnanf( NaN )\n true\n > bool = base.isnanf( 7.0 )\n false\n\n See Also\n --------\n base.isnan\n","base.isNegativeFinite":"\nbase.isNegativeFinite( x )\n Tests if a double-precision floating-point numeric value is a negative\n finite number.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether the value is a negative finite number.\n\n Examples\n --------\n > var bool = base.isNegativeFinite( -3.14 )\n true\n > bool = base.isNegativeFinite( -Infinity )\n false\n > bool = base.isNegativeFinite( 2.0 )\n false\n > bool = base.isNegativeFinite( NaN )\n false\n > bool = base.isNegativeFinite( -0.0 )\n false\n\n See Also\n --------\n base.isPositiveFinite, base.isNonNegativeFinite, base.isNonPositiveFinite\n","base.isNegativeInteger":"\nbase.isNegativeInteger( x )\n Tests if a finite double-precision floating-point number is a negative\n integer.\n\n The function assumes a finite number. If provided negative infinity, the\n function will return `true`, when, in fact, the result is undefined.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a negative integer.\n\n Examples\n --------\n > var bool = base.isNegativeInteger( -1.0 )\n true\n > bool = base.isNegativeInteger( 0.0 )\n false\n > bool = base.isNegativeInteger( 10.0 )\n false\n\n See Also\n --------\n base.isInteger, base.isNonNegativeInteger, base.isNonPositiveInteger, base.isPositiveInteger\n","base.isNegativeZero":"\nbase.isNegativeZero( x )\n Tests if a double-precision floating-point numeric value is negative zero.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is negative zero.\n\n Examples\n --------\n > var bool = base.isNegativeZero( -0.0 )\n true\n > bool = base.isNegativeZero( 0.0 )\n false\n\n See Also\n --------\n base.isPositiveZero\n","base.isNegativeZerof":"\nbase.isNegativeZerof( x )\n Tests if a single-precision floating-point numeric value is negative zero.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is negative zero.\n\n Examples\n --------\n > var bool = base.isNegativeZerof( -0.0 )\n true\n > bool = base.isNegativeZerof( 0.0 )\n false\n\n See Also\n --------\n base.isNegativeZero, base.isPositiveZerof\n","base.isNonNegativeFinite":"\nbase.isNonNegativeFinite( x )\n Tests if a double-precision floating-point numeric value is a nonnegative\n finite number.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a nonnegative finite number.\n\n Examples\n --------\n > var out = base.isNonNegativeFinite( 5.0 )\n true\n > out = base.isNonNegativeFinite( 3.14 )\n true\n > out = base.isNonNegativeFinite( 0.0 )\n true\n > out = base.isNonNegativeFinite( Infinity )\n false\n > out = base.isNonNegativeFinite( -3.14 )\n false\n > out = base.isNonNegativeFinite( NaN )\n false\n\n See Also\n --------\n base.isNegativeFinite, base.isPositiveFinite, base.isNonPositiveFinite\n","base.isNonNegativeInteger":"\nbase.isNonNegativeInteger( x )\n Tests if a finite double-precision floating-point number is a nonnegative\n integer.\n\n The function assumes a finite number. If provided positive infinity, the\n function will return `true`, when, in fact, the result is undefined.\n\n The function does not distinguish between positive and negative zero.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a nonnegative integer.\n\n Examples\n --------\n > var bool = base.isNonNegativeInteger( 1.0 )\n true\n > bool = base.isNonNegativeInteger( 0.0 )\n true\n > bool = base.isNonNegativeInteger( -10.0 )\n false\n\n See Also\n --------\n base.isInteger, base.isNegativeInteger, base.isNonPositiveInteger, base.isPositiveInteger\n","base.isNonPositiveFinite":"\nbase.isNonPositiveFinite( x )\n Tests if a double-precision floating-point numeric value is a nonpositive\n finite number.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n out: boolean\n Boolean indicating whether the value is a nonpositive finite number.\n\n Examples\n --------\n > var bool = base.isNonPositiveFinite( -3.14 )\n true\n > var bool = base.isNonPositiveFinite( 0.0 )\n true\n > var bool = base.isNonPositiveFinite( -Infinity )\n false\n > var bool = base.isNonPositiveFinite( 3.14 )\n false\n > var bool = base.isNonPositiveFinite( NaN )\n false\n\n See Also\n --------\n base.isNegativeFinite, base.isPositiveFinite, base.isNonNegativeFinite\n","base.isNonPositiveInteger":"\nbase.isNonPositiveInteger( x )\n Tests if a finite double-precision floating-point number is a nonpositive\n integer.\n\n The function assumes a finite number. If provided negative infinity, the\n function will return `true`, when, in fact, the result is undefined.\n\n The function does not distinguish between positive and negative zero.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a nonpositive integer.\n\n Examples\n --------\n > var bool = base.isNonPositiveInteger( -1.0 )\n true\n > bool = base.isNonPositiveInteger( 0.0 )\n true\n > bool = base.isNonPositiveInteger( 10.0 )\n false\n\n See Also\n --------\n base.isInteger, base.isNegativeInteger, base.isNonNegativeInteger, base.isPositiveInteger\n","base.isOdd":"\nbase.isOdd( x )\n Tests if a finite numeric value is an odd number.\n\n The function assumes a finite number. If provided positive or negative\n infinity, the function will return `true`, when, in fact, the result is\n undefined.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is an odd number.\n\n Examples\n --------\n > var bool = base.isOdd( 5.0 )\n true\n > bool = base.isOdd( -2.0 )\n false\n > bool = base.isOdd( 0.0 )\n false\n > bool = base.isOdd( NaN )\n false\n\n See Also\n --------\n base.isEven\n","base.isOddInt32":"\nbase.isOddInt32( x )\n Tests if a 32-bit integer is odd.\n\n Parameters\n ----------\n x: integer\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is an odd number.\n\n Examples\n --------\n > var bool = base.isOddInt32( 5 )\n true\n > bool = base.isOddInt32( -2 )\n false\n > bool = base.isOddInt32( 0 )\n false\n\n See Also\n --------\n base.isEvenInt32, base.isOdd\n","base.isPositiveFinite":"\nbase.isPositiveFinite( x )\n Tests if a double-precision floating-point numeric value is a positive\n finite number.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a positive finite number.\n\n Examples\n --------\n > var bool = base.isPositiveFinite( 5.0 )\n true\n > bool = base.isPositiveFinite( 3.14 )\n true\n > bool = base.isPositiveFinite( 0.0 )\n false\n > bool = base.isPositiveFinite( Infinity )\n false\n > bool = base.isPositiveFinite( -3.14 )\n false\n > bool = base.isPositiveFinite( NaN )\n false\n\n See Also\n --------\n base.isNegativeFinite, base.isNonNegativeFinite, base.isNonPositiveFinite\n","base.isPositiveInteger":"\nbase.isPositiveInteger( x )\n Tests if a finite double-precision floating-point number is a positive\n integer.\n\n The function assumes a finite number. If provided positive infinity, the\n function will return `true`, when, in fact, the result is undefined.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a positive integer.\n\n Examples\n --------\n > var bool = base.isPositiveInteger( 1.0 )\n true\n > bool = base.isPositiveInteger( 0.0 )\n false\n > bool = base.isPositiveInteger( -10.0 )\n false\n\n See Also\n --------\n base.isInteger, base.isNegativeInteger, base.isNonNegativeInteger, base.isNonPositiveInteger\n","base.isPositiveZero":"\nbase.isPositiveZero( x )\n Tests if a double-precision floating-point numeric value is positive zero.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is positive zero.\n\n Examples\n --------\n > var bool = base.isPositiveZero( 0.0 )\n true\n > bool = base.isPositiveZero( -0.0 )\n false\n\n See Also\n --------\n base.isNegativeZero\n","base.isPositiveZerof":"\nbase.isPositiveZerof( x )\n Tests if a single-precision floating-point numeric value is positive zero.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is positive zero.\n\n Examples\n --------\n > var bool = base.isPositiveZerof( 0.0 )\n true\n > bool = base.isPositiveZerof( -0.0 )\n false\n\n See Also\n --------\n base.isNegativeZerof, base.isPositiveZero\n","base.isPow2Uint32":"\nbase.isPow2Uint32( x )\n Tests whether an unsigned integer is a power of 2.\n\n Parameters\n ----------\n x: integer\n Unsigned integer.\n\n Returns\n -------\n bool: boolean\n Boolean indicating if a value is a power of 2.\n\n Examples\n --------\n > var bool = base.isPow2Uint32( 2 )\n true\n > bool = base.isPow2Uint32( 5 )\n false\n\n","base.isPrime":"\nbase.isPrime( x )\n Tests if a number is prime.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a prime number.\n\n Examples\n --------\n > var bool = base.isPrime( 11.0 )\n true\n > bool = base.isPrime( 3.14 )\n false\n\n See Also\n --------\n base.isComposite, base.isInteger\n","base.isProbability":"\nbase.isProbability( x )\n Tests if a double-precision floating-point number value is a probability.\n\n A probability is defined as a number on the closed interval [0,1].\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a probability.\n\n Examples\n --------\n > var bool = base.isProbability( 0.5 )\n true\n > bool = base.isProbability( 3.14 )\n false\n > bool = base.isProbability( NaN )\n false\n\n","base.isSafeInteger":"\nbase.isSafeInteger( x )\n Tests if a finite double-precision floating-point number is a safe integer.\n\n An integer valued number is \"safe\" when the number can be exactly\n represented as a double-precision floating-point number.\n\n Parameters\n ----------\n x: number\n Value to test.\n\n Returns\n -------\n bool: boolean\n Boolean indicating whether the value is a safe integer.\n\n Examples\n --------\n > var bool = base.isSafeInteger( 1.0 )\n true\n > bool = base.isSafeInteger( 2.0e200 )\n false\n > bool = base.isSafeInteger( 3.14 )\n false\n\n","base.kebabcase":"\nbase.kebabcase( str )\n Converts a string to kebab case.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Kebab-cased string.\n\n Examples\n --------\n > var out = base.kebabcase( 'Hello World!' )\n 'hello-world'\n > out = base.kebabcase( 'I am a tiny little teapot' )\n 'i-am-a-tiny-little-teapot'\n\n See Also\n --------\n base.camelcase, base.lowercase, base.pascalcase, base.snakecase, base.uppercase","base.kernelBetainc":"\nbase.kernelBetainc( x, a, b, regularized, upper )\n Computes the kernel function for the regularized incomplete beta function.\n\n The `regularized` and `upper` parameters specify whether to evaluate the\n non-regularized and/or upper incomplete beta functions, respectively.\n\n If provided `x < 0` or `x > 1`, the function returns `[ NaN, NaN ]`.\n\n If provided `a < 0` or `b < 0`, the function returns `[ NaN, NaN ]`.\n\n If provided `NaN` for `x`, `a`, or `b`, the function returns `[ NaN, NaN ]`.\n\n Parameters\n ----------\n x: number\n First function parameter.\n\n a: number\n Second function parameter.\n\n b: number\n Third function parameter.\n\n regularized: boolean\n Boolean indicating whether the function should evaluate the regularized\n or non-regularized incomplete beta function.\n\n upper: boolean\n Boolean indicating whether the function should return the upper tail of\n the incomplete beta function.\n\n Returns\n -------\n y: Array|TypedArray|Object\n Function value and first derivative.\n\n Examples\n --------\n > var out = base.kernelBetainc( 0.8, 1.0, 0.3, false, false )\n [ ~1.277, ~0.926 ]\n > out = base.kernelBetainc( 0.2, 1.0, 2.0, true, false )\n [ 0.36, 1.6 ]\n\n\nbase.kernelBetainc.assign( x, a, b, regularized, upper, out, stride, offset )\n Computes the kernel function for the regularized incomplete beta function.\n\n The `regularized` and `upper` parameters specify whether to evaluate the\n non-regularized and/or upper incomplete beta functions, respectively.\n\n If provided `x < 0` or `x > 1`, the function returns `[ NaN, NaN ]`.\n\n If provided `a < 0` or `b < 0`, the function returns `[ NaN, NaN ]`.\n\n If provided `NaN` for `x`, `a`, or `b`, the function returns `[ NaN, NaN ]`.\n\n Parameters\n ----------\n x: number\n First function parameter.\n\n a: number\n Second function parameter.\n\n b: number\n Third function parameter.\n\n regularized: boolean\n Boolean indicating whether the function should evaluate the regularized\n or non-regularized incomplete beta function.\n\n upper: boolean\n Boolean indicating whether the function should return the upper tail of\n the incomplete beta function.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n y: Array|TypedArray|Object\n Function value and first derivative.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.kernelBetainc.assign( 0.2, 1.0, 2.0, true, true, out, 1, 0 )\n [ 0.64, 1.6 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.betainc\n","base.kernelBetainc.assign":"\nbase.kernelBetainc.assign( x, a, b, regularized, upper, out, stride, offset )\n Computes the kernel function for the regularized incomplete beta function.\n\n The `regularized` and `upper` parameters specify whether to evaluate the\n non-regularized and/or upper incomplete beta functions, respectively.\n\n If provided `x < 0` or `x > 1`, the function returns `[ NaN, NaN ]`.\n\n If provided `a < 0` or `b < 0`, the function returns `[ NaN, NaN ]`.\n\n If provided `NaN` for `x`, `a`, or `b`, the function returns `[ NaN, NaN ]`.\n\n Parameters\n ----------\n x: number\n First function parameter.\n\n a: number\n Second function parameter.\n\n b: number\n Third function parameter.\n\n regularized: boolean\n Boolean indicating whether the function should evaluate the regularized\n or non-regularized incomplete beta function.\n\n upper: boolean\n Boolean indicating whether the function should return the upper tail of\n the incomplete beta function.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n y: Array|TypedArray|Object\n Function value and first derivative.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.kernelBetainc.assign( 0.2, 1.0, 2.0, true, true, out, 1, 0 )\n [ 0.64, 1.6 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.betainc","base.kernelBetaincinv":"\nbase.kernelBetaincinv( a, b, p, q )\n Computes the inverse of the lower incomplete beta function.\n\n Probabilities `p` and `q` must satisfy `p = 1 - q`.\n\n Parameters\n ----------\n a: number\n First function parameter (a positive number).\n\n b: number\n Second function parameter (a positive number).\n\n p: number\n Probability.\n\n q: number\n Probability equal to `1-p`.\n\n Returns\n -------\n out: Array\n Two-element array holding function value `y` and `1-y`.\n\n Examples\n --------\n > var y = base.kernelBetaincinv( 3.0, 3.0, 0.2, 0.8 )\n [ ~0.327, ~0.673 ]\n > y = base.kernelBetaincinv( 3.0, 3.0, 0.4, 0.6 )\n [ ~0.446, ~0.554 ]\n > y = base.kernelBetaincinv( 1.0, 6.0, 0.4, 0.6 )\n [ ~0.082, ~0.918 ]\n > y = base.kernelBetaincinv( 1.0, 6.0, 0.8, 0.2 )\n [ ~0.235, ~0.765 ]\n\n See Also\n --------\n base.betaincinv\n","base.kernelCos":"\nbase.kernelCos( x, y )\n Computes the cosine of a double-precision floating-point number on the\n interval [-π/4, π/4].\n\n For increased accuracy, the number for which the cosine should be evaluated\n can be supplied as a double-double number (i.e., a non-evaluated sum of two\n double-precision floating-point numbers `x` and `y`).\n\n The two numbers must satisfy `|y| < 0.5 * ulp( x )`.\n\n If either argument is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n y: number\n Tail of `x`.\n\n Returns\n -------\n out: number\n Cosine.\n\n Examples\n --------\n > var out = base.kernelCos( 0.0, 0.0 )\n ~1.0\n > out = base.kernelCos( PI/6.0, 0.0 )\n ~0.866\n > out = base.kernelCos( 0.785, -1.144e-17 )\n ~0.707\n > out = base.kernelCos( NaN )\n NaN\n\n See Also\n --------\n base.cos, base.kernelSin, base.kernelTan\n","base.kernelLog1p":"\nbase.kernelLog1p( f )\n Computes `log(1+f) - f` for `1+f` in ~[sqrt(2)/2, sqrt(2)].\n\n This function provides a common means for computing logarithms in base e.\n Argument reduction and adding the final term of the polynomial must be done\n by the caller for increased accuracy when different bases are used.\n\n Parameters\n ----------\n f: number\n Input value.\n\n Returns\n -------\n out: number\n Function value.\n\n Examples\n --------\n > var y = base.kernelLog1p( 1.0 )\n ~0.1931\n > y = base.kernelLog1p( 1.4142135623730951 )\n ~0.4672\n > y = base.kernelLog1p( NaN )\n NaN\n\n See Also\n --------\n base.log1p\n","base.kernelSin":"\nbase.kernelSin( x, y )\n Computes the sine of a double-precision floating-point number on [-π/4,π/4].\n\n For increased accuracy, the number for which the cosine should be evaluated\n can be supplied as a double-double number (i.e., a non-evaluated sum of two\n double-precision floating-point numbers `x` and `y`).\n\n The two numbers must satisfy `|y| < 0.5 * ulp( x )`.\n\n If either argument is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n y: number\n Tail of `x`.\n\n Returns\n -------\n out: number\n Sine.\n\n Examples\n --------\n > var y = base.kernelSin( 0.0, 0.0 )\n ~0.0\n > y = base.kernelSin( PI/6.0, 0.0 )\n ~0.5\n > y = base.kernelSin( 0.619, 9.279e-18 )\n ~0.58\n\n > y = base.kernelSin( NaN, 0.0 )\n NaN\n > y = base.kernelSin( 2.0, NaN )\n NaN\n > y = base.kernelSin( NaN, NaN )\n NaN\n\n See Also\n --------\n base.kernelCos, base.kernelTan, base.sin\n","base.kernelTan":"\nbase.kernelTan( x, y, k )\n Computes the tangent of a double-precision floating-point number on the\n interval [-π/4, π/4].\n\n For increased accuracy, the number for which the tangent should be evaluated\n can be supplied as a double-double number (i.e., a non-evaluated sum of two\n double-precision floating-point numbers `x` and `y`).\n\n The numbers `x` and `y` must satisfy `|y| < 0.5 * ulp( x )`.\n\n If either `x` or `y` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value (in radians).\n\n y: number\n Tail of `x`.\n\n k: integer\n If `k=1`, the function returns `tan(x+y)`. If `k=-1`, the function\n returns the negative inverse `-1/tan(x+y)`.\n\n Returns\n -------\n out: number\n Tangent.\n\n Examples\n --------\n > var out = base.kernelTan( PI/4.0, 0.0, 1 )\n ~1.0\n > out = base.kernelTan( PI/4.0, 0.0, -1 )\n ~-1.0\n > out = base.kernelTan( PI/6.0, 0.0, 1 )\n ~0.577\n > out = base.kernelTan( 0.664, 5.288e-17, 1 )\n ~0.783\n\n > out = base.kernelTan( NaN, 0.0, 1 )\n NaN\n > out = base.kernelTan( 3.0, NaN, 1 )\n NaN\n > out = base.kernelTan( 3.0, 0.0, NaN )\n NaN\n\n See Also\n --------\n base.kernelCos, base.kernelSin, base.tan\n","base.kroneckerDelta":"\nbase.kroneckerDelta( i, j )\n Evaluates the Kronecker delta.\n\n If `i == j`, the function returns `1`; otherwise, the function returns zero.\n\n Parameters\n ----------\n i: number\n Input value.\n\n j: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.kroneckerDelta( 3.14, 0.0 )\n 0.0\n > y = base.kroneckerDelta( 3.14, 3.14 )\n 1.0\n\n See Also\n --------\n base.diracDelta\n","base.kroneckerDeltaf":"\nbase.kroneckerDeltaf( i, j )\n Evaluates the Kronecker delta (single-precision).\n\n If `i == j`, the function returns `1`; otherwise, the function returns zero.\n\n Parameters\n ----------\n i: number\n Input value.\n\n j: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.kroneckerDeltaf( 3.14, 0.0 )\n 0.0\n > y = base.kroneckerDeltaf( 3.14, 3.14 )\n 1.0\n\n See Also\n --------\n base.kroneckerDelta\n","base.labs":"\nbase.labs( x )\n Computes an absolute value of a signed 32-bit integer in two's complement\n format.\n\n Parameters\n ----------\n x: integer\n Signed 32-bit integer.\n\n Returns\n -------\n out: integer\n Absolute value.\n\n Examples\n --------\n > var v = base.labs( -1|0 )\n 1\n > v = base.labs( 2|0 )\n 2\n > v = base.labs( 0|0 )\n 0\n\n See Also\n --------\n base.abs\n","base.last":"\nbase.last( str, n )\n Returns the last `n` UTF-16 code units of a string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n n: integer\n Number of UTF-16 code units to return.\n\n Returns\n -------\n out: string\n Output string.\n\n Examples\n --------\n > var out = base.last( 'hello', 1 )\n 'o'\n > out = base.last( 'JavaScript', 6 )\n 'Script'\n > out = base.last( 'foo bar', 10 )\n 'foo bar'\n\n See Also\n --------\n base.firstCodeUnit, base.lastCodePoint, base.lastGraphemeCluster\n","base.lastCodePoint":"\nbase.lastCodePoint( str, n )\n Returns the last `n` Unicode code points of a string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n n: integer\n Number of Unicode code points to return.\n\n Returns\n -------\n out: string\n Output string.\n\n Examples\n --------\n > var out = base.lastCodePoint( 'hello world', 1 )\n 'd'\n > out = base.lastCodePoint( 'JavaScript', 6 )\n 'Script'\n > out = base.lastCodePoint( 'अनुच्छेद', 1 )\n 'द'\n\n See Also\n --------\n base.firstCodePoint, base.lastGraphemeCluster, base.last\n","base.lastGraphemeCluster":"\nbase.lastGraphemeCluster( str, n )\n Returns the last `n` grapheme clusters (i.e., user-perceived characters) of\n a string.\n\n Parameters\n ----------\n str: string\n Input string.\n\n n: integer\n Number of grapheme clusters to return.\n\n Returns\n -------\n out: string\n Output string.\n\n Examples\n --------\n > var out = base.lastGraphemeCluster( 'beep', 1 )\n 'p'\n > out = base.lastGraphemeCluster( 'Boop', 2 )\n 'op'\n > out = base.lastGraphemeCluster( 'JavaScript', 6 )\n 'Script'\n\n See Also\n --------\n base.firstGraphemeCluster, base.lastCodePoint, base.last\n","base.lcm":"\nbase.lcm( a, b )\n Computes the least common multiple (lcm).\n\n If either `a` or `b` is `0`, the function returns `0`.\n\n Both `a` and `b` must have integer values; otherwise, the function returns\n `NaN`.\n\n Parameters\n ----------\n a: integer\n First integer.\n\n b: integer\n Second integer.\n\n Returns\n -------\n out: integer\n Least common multiple.\n\n Examples\n --------\n > var v = base.lcm( 21, 6 )\n 42\n\n See Also\n --------\n base.gcd\n","base.ldexp":"\nbase.ldexp( frac, exp )\n Multiplies a double-precision floating-point number by an integer power of\n two; i.e., `x = frac * 2^exp`.\n\n If `frac` equals positive or negative `zero`, `NaN`, or positive or negative\n infinity, the function returns a value equal to `frac`.\n\n Parameters\n ----------\n frac: number\n Fraction.\n\n exp: number\n Exponent.\n\n Returns\n -------\n out: number\n Double-precision floating-point number equal to `frac * 2^exp`.\n\n Examples\n --------\n > var x = base.ldexp( 0.5, 3 )\n 4.0\n > x = base.ldexp( 4.0, -2 )\n 1.0\n > x = base.ldexp( 0.0, 20 )\n 0.0\n > x = base.ldexp( -0.0, 39 )\n -0.0\n > x = base.ldexp( NaN, -101 )\n NaN\n > x = base.ldexp( PINF, 11 )\n Infinity\n > x = base.ldexp( NINF, -118 )\n -Infinity\n\n See Also\n --------\n base.frexp\n","base.leftPad":"\nbase.leftPad( str, len, pad )\n Left pads a string such that the padded string has a length of at least\n `len`.\n\n An output string is not guaranteed to have a length of exactly `len`, but to\n have a length of at least `len`. To generate a padded string having a length\n equal to `len`, post-process a padded string by trimming off excess\n characters.\n\n Parameters\n ----------\n str: string\n Input string.\n\n len: integer\n Minimum string length.\n\n pad: string\n String used to pad.\n\n Returns\n -------\n out: string\n Padded string.\n\n Examples\n --------\n > var out = base.leftPad( 'a', 5, ' ' )\n ' a'\n > out = base.leftPad( 'beep', 10, 'b' )\n 'bbbbbbbeep'\n > out = base.leftPad( 'boop', 12, 'beep' )\n 'beepbeepboop'\n\n See Also\n --------\n base.rightPad\n","base.leftTrim":"\nbase.leftTrim( str )\n Trims whitespace from the beginning of a string.\n\n \"Whitespace\" is defined as the following characters:\n\n - \\f\n - \\n\n - \\r\n - \\t\n - \\v\n - \\u0020\n - \\u00a0\n - \\u1680\n - \\u2000-\\u200a\n - \\u2028\n - \\u2029\n - \\u202f\n - \\u205f\n - \\u3000\n - \\ufeff\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Trimmed string.\n\n Examples\n --------\n > var out = base.leftTrim( ' \\r\\n\\t Beep \\t\\t\\n ' )\n 'Beep \\t\\t\\n '\n\n See Also\n --------\n base.rightTrim, base.trim\n","base.ln":"\nbase.ln( x )\n Evaluates the natural logarithm of a double-precision floating-point number.\n\n For negative numbers, the natural logarithm is not defined.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.ln( 4.0 )\n ~1.386\n > y = base.ln( 0.0 )\n -Infinity\n > y = base.ln( PINF )\n Infinity\n > y = base.ln( NaN )\n NaN\n > y = base.ln( -4.0 )\n NaN\n\n See Also\n --------\n base.exp, base.log10, base.log1p, base.log2\n","base.log":"\nbase.log( x, b )\n Computes the base `b` logarithm of `x`.\n\n For negative `b` or `x`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n b: number\n Base.\n\n Returns\n -------\n y: number\n Logarithm (base `b`).\n\n Examples\n --------\n > var y = base.log( 100.0, 10.0 )\n 2.0\n > y = base.log( 16.0, 2.0 )\n 4.0\n > y = base.log( 5.0, 1.0 )\n Infinity\n > y = base.log( NaN, 2.0 )\n NaN\n > y = base.log( 1.0, NaN )\n NaN\n > y = base.log( -4.0, 2.0 )\n NaN\n > y = base.log( 4.0, -2.0 )\n NaN\n\n See Also\n --------\n base.exp, base.ln, base.log10, base.log1p, base.log2\n","base.log1mexp":"\nbase.log1mexp( x )\n Evaluates the natural logarithm of `1-exp(-|x|)`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.log1mexp( -10.0 )\n ~-0.00005\n > y = base.log1mexp( 0.0 )\n -Infinity\n > y = base.log1mexp( 5.0 )\n ~-0.00676\n > y = base.log1mexp( 10.0 )\n ~-0.00005\n > y = base.log1mexp( NaN )\n NaN\n\n See Also\n --------\n base.exp, base.ln, base.log1p, base.log1pexp","base.log1p":"\nbase.log1p( x )\n Evaluates the natural logarithm of `1+x`.\n\n For `x < -1`, the function returns `NaN`, as the natural logarithm is not\n defined for negative numbers.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.log1p( 4.0 )\n ~1.609\n > y = base.log1p( -1.0 )\n -Infinity\n > y = base.log1p( 0.0 )\n 0.0\n > y = base.log1p( -0.0 )\n -0.0\n > y = base.log1p( -2.0 )\n NaN\n > y = base.log1p( NaN )\n NaN\n\n See Also\n --------\n base.ln, base.log\n","base.log1pexp":"\nbase.log1pexp( x )\n Evaluates the natural logarithm of `1+exp(x)`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.log1pexp( -10.0 )\n ~0.000045\n > y = base.log1pexp( 0.0 )\n ~0.693147\n > y = base.log1pexp( 5.0 )\n ~5.006715\n > y = base.log1pexp( 34.0 )\n 34.0\n > y = base.log1pexp( NaN )\n NaN\n\n See Also\n --------\n base.exp, base.ln, base.log1mexp, base.log1p","base.log1pmx":"\nbase.log1pmx( x )\n Evaluates `ln(1+x) - x`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > base.log1pmx( 1.1 )\n ~-0.358\n > base.log1pmx( 0.99 )\n ~-0.302\n > base.log1pmx( -0.99 )\n ~-3.615\n > base.log1pmx( -1.1 )\n NaN\n > base.log1pmx( NaN )\n NaN\n\n See Also\n --------\n base.ln, base.log1p","base.log2":"\nbase.log2( x )\n Evaluates the binary logarithm (base two).\n\n For negative numbers, the binary logarithm is not defined.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.log2( 4.0 )\n 2.0\n > y = base.log2( 8.0 )\n 3.0\n > y = base.log2( 0.0 )\n -Infinity\n > y = base.log2( PINF )\n Infinity\n > y = base.log2( NaN )\n NaN\n > y = base.log2( -4.0 )\n NaN\n\n See Also\n --------\n base.exp2, base.ln, base.log\n","base.log10":"\nbase.log10( x )\n Evaluates the common logarithm (base 10).\n\n For negative numbers, the common logarithm is not defined.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.log10( 100.0 )\n 2.0\n > y = base.log10( 8.0 )\n ~0.903\n > y = base.log10( 0.0 )\n -Infinity\n > y = base.log10( PINF )\n Infinity\n > y = base.log10( NaN )\n NaN\n > y = base.log10( -4.0 )\n NaN\n\n See Also\n --------\n base.exp10, base.ln, base.log\n","base.logaddexp":"\nbase.logaddexp( x, y )\n Computes the natural logarithm of `exp(x) + exp(y)`.\n\n Parameters\n ----------\n x: number\n Input value.\n\n y: number\n Input value.\n\n Returns\n -------\n v: number\n Function value.\n\n Examples\n --------\n > var v = base.logaddexp( 90.0, 90.0 )\n ~90.6931\n > v = base.logaddexp( -20.0, 90.0 )\n 90.0\n > v = base.logaddexp( 0.0, -100.0 )\n ~3.7201e-44\n > v = base.logaddexp( NaN, NaN )\n NaN\n\n See Also\n --------\n base.exp, base.ln\n","base.logit":"\nbase.logit( p )\n Evaluates the logit function.\n\n Let `p` be the probability of some event. The logit function is defined as\n the logarithm of the odds `p / (1-p)`.\n\n If `p < 0` or `p > 1`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.logit( 0.2 )\n ~-1.386\n > y = base.logit( 0.9 )\n ~2.197\n > y = base.logit( -4.0 )\n NaN\n > y = base.logit( 1.5 )\n NaN\n > y = base.logit( NaN )\n NaN\n\n","base.lowercase":"\nbase.lowercase( str )\n Converts a string to lowercase.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Lowercase string.\n\n Examples\n --------\n > var out = base.lowercase( 'bEEp' )\n 'beep'\n\n See Also\n --------\n base.snakecase, base.uppercase\n","base.lucas":"\nbase.lucas( n )\n Computes the nth Lucas number.\n\n Lucas numbers follow the recurrence relation\n\n L_n = L_{n-1} + L_{n-2}\n\n with seed values L_0 = 2 and L_1 = 1.\n\n If `n` is greater than `76`, the function returns `NaN`, as larger Lucas\n numbers cannot be accurately represented due to limitations of double-\n precision floating-point format.\n\n If not provided a nonnegative integer value, the function returns `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Input value.\n\n Returns\n -------\n y: integer\n Lucas number.\n\n Examples\n --------\n > var y = base.lucas( 0 )\n 2\n > y = base.lucas( 1 )\n 1\n > y = base.lucas( 2 )\n 3\n > y = base.lucas( 3 )\n 4\n > y = base.lucas( 4 )\n 7\n > y = base.lucas( 77 )\n NaN\n > y = base.lucas( NaN )\n NaN\n\n See Also\n --------\n base.fibonacci, base.negalucas\n","base.lucaspoly":"\nbase.lucaspoly( n, x )\n Evaluates a Lucas polynomial.\n\n Parameters\n ----------\n n: integer\n Lucas polynomial to evaluate.\n\n x: number\n Value at which to evaluate the Lucas polynomial.\n\n Returns\n -------\n out: number\n Evaluated Lucas polynomial.\n\n Examples\n --------\n // 2^5 + 5*2^3 + 5*2\n > var v = base.lucaspoly( 5, 2.0 )\n 82.0\n\n\nbase.lucaspoly.factory( n )\n Returns a function for evaluating a Lucas polynomial.\n\n Parameters\n ----------\n n: integer\n Lucas polynomial to evaluate.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a Lucas polynomial.\n\n Examples\n --------\n > var polyval = base.lucaspoly.factory( 5 );\n\n // 1^5 + 5*1^2 + 5\n > var v = polyval( 1.0 )\n 11.0\n\n // 2^5 + 5*2^3 + 5*2\n > v = polyval( 2.0 )\n 82.0\n\n See Also\n --------\n base.evalpoly, base.fibpoly\n","base.lucaspoly.factory":"\nbase.lucaspoly.factory( n )\n Returns a function for evaluating a Lucas polynomial.\n\n Parameters\n ----------\n n: integer\n Lucas polynomial to evaluate.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a Lucas polynomial.\n\n Examples\n --------\n > var polyval = base.lucaspoly.factory( 5 );\n\n // 1^5 + 5*1^2 + 5\n > var v = polyval( 1.0 )\n 11.0\n\n // 2^5 + 5*2^3 + 5*2\n > v = polyval( 2.0 )\n 82.0\n\n See Also\n --------\n base.evalpoly, base.fibpoly","base.max":"\nbase.max( x, y )\n Returns the maximum value.\n\n If any argument is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: number\n Maximum value.\n\n Examples\n --------\n > var v = base.max( 3.14, 4.2 )\n 4.2\n > v = base.max( 3.14, NaN )\n NaN\n > v = base.max( +0.0, -0.0 )\n +0.0\n\n See Also\n --------\n base.maxabs, base.maxn, base.min\n","base.maxabs":"\nbase.maxabs( x, y )\n Returns the maximum absolute value.\n\n If any argument is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: number\n Maximum absolute value.\n\n Examples\n --------\n > var v = base.maxabs( 3.14, -4.2 )\n 4.2\n > v = base.maxabs( 3.14, NaN )\n NaN\n > v = base.maxabs( +0.0, -0.0 )\n +0.0\n\n See Also\n --------\n base.max, base.minabs\n","base.maxabsn":"\nbase.maxabsn( [x[, y[, ...args]]] )\n Returns the maximum absolute value.\n\n If any argument is `NaN`, the function returns `NaN`.\n\n When an empty set is considered a subset of the extended reals (all real\n numbers, including positive and negative infinity), negative infinity is the\n least upper bound. Similar to zero being the identity element for the sum of\n an empty set and to one being the identity element for the product of an\n empty set, negative infinity is the identity element for the maximum, and\n thus, if not provided any arguments, the function returns `+infinity` (i.e.,\n the absolute value of `-infinity`).\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n Returns\n -------\n out: number\n Maximum absolute value.\n\n Examples\n --------\n > var v = base.maxabsn( 3.14, -4.2 )\n 4.2\n > v = base.maxabsn( 5.9, 3.14, 4.2 )\n 5.9\n > v = base.maxabsn( 3.14, NaN )\n NaN\n > v = base.maxabsn( +0.0, -0.0 )\n +0.0\n\n See Also\n --------\n base.maxn, base.maxabs, base.minabsn\n","base.maxn":"\nbase.maxn( [x[, y[, ...args]]] )\n Returns the maximum value.\n\n If any argument is `NaN`, the function returns `NaN`.\n\n When an empty set is considered a subset of the extended reals (all real\n numbers, including positive and negative infinity), negative infinity is the\n least upper bound. Similar to zero being the identity element for the sum of\n an empty set and to one being the identity element for the product of an\n empty set, negative infinity is the identity element for the maximum, and\n thus, if not provided any arguments, the function returns negative infinity.\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n Returns\n -------\n out: number\n Maximum value.\n\n Examples\n --------\n > var v = base.maxn( 3.14, 4.2 )\n 4.2\n > v = base.maxn( 5.9, 3.14, 4.2 )\n 5.9\n > v = base.maxn( 3.14, NaN )\n NaN\n > v = base.maxn( +0.0, -0.0 )\n +0.0\n\n See Also\n --------\n base.max, base.maxabsn, base.minn\n","base.min":"\nbase.min( x, y )\n Returns the minimum value.\n\n If any argument is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: number\n Minimum value.\n\n Examples\n --------\n > var v = base.min( 3.14, 4.2 )\n 3.14\n > v = base.min( 3.14, NaN )\n NaN\n > v = base.min( +0.0, -0.0 )\n -0.0\n\n See Also\n --------\n base.max, base.minabs, base.minn\n","base.minabs":"\nbase.minabs( x, y )\n Returns the minimum absolute value.\n\n If any argument is `NaN`, the function returns `NaN`.\n\n When an empty set is considered a subset of the extended reals (all real\n numbers, including positive and negative infinity), positive infinity is the\n greatest upper bound. Similar to zero being the identity element for the sum\n of an empty set and to one being the identity element for the product of an\n empty set, positive infinity is the identity element for the minimum, and\n thus, if not provided any arguments, the function returns positive infinity.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: number\n Minimum absolute value.\n\n Examples\n --------\n > var v = base.minabs( 3.14, -4.2 )\n 3.14\n > v = base.minabs( 3.14, NaN )\n NaN\n > v = base.minabs( +0.0, -0.0 )\n +0.0\n\n See Also\n --------\n base.maxabs, base.min\n","base.minabsn":"\nbase.minabsn( [x[, y[, ...args]]] )\n Returns the minimum absolute value.\n\n If any argument is `NaN`, the function returns `NaN`.\n\n When an empty set is considered a subset of the extended reals (all real\n numbers, including positive and negative infinity), positive infinity is the\n greatest upper bound. Similar to zero being the identity element for the sum\n of an empty set and to one being the identity element for the product of an\n empty set, positive infinity is the identity element for the minimum, and\n thus, if not provided any arguments, the function returns positive infinity.\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n Returns\n -------\n out: number\n Minimum absolute value.\n\n Examples\n --------\n > var v = base.minabsn( 3.14, -4.2 )\n 3.14\n > v = base.minabsn( 5.9, 3.14, 4.2 )\n 3.14\n > v = base.minabsn( 3.14, NaN )\n NaN\n > v = base.minabsn( +0.0, -0.0 )\n +0.0\n\n See Also\n --------\n base.maxabsn, base.minn, base.minabs\n","base.minmax":"\nbase.minmax( x, y )\n Returns the minimum and maximum values.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum values.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: Array\n Minimum and maximum values.\n\n Examples\n --------\n > var v = base.minmax( 3.14, 4.2 )\n [ 3.14, 4.2 ]\n > v = base.minmax( 3.14, NaN )\n [ NaN, NaN ]\n > v = base.minmax( +0.0, -0.0 )\n [ -0.0, +0.0 ]\n\n\nbase.minmax.assign( x, y, out, stride, offset )\n Returns the minimum and maximum values and assigns results to a provided\n output array.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum values.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n out: Array|TypedArray|Object\n Output object.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Minimum and maximum values.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.minmax.assign( 3.14, -1.5, out, 1, 0 )\n [ -1.5, 3.14 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.max, base.min, base.minmaxabs","base.minmax.assign":"\nbase.minmax.assign( x, y, out, stride, offset )\n Returns the minimum and maximum values and assigns results to a provided\n output array.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum values.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n out: Array|TypedArray|Object\n Output object.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Minimum and maximum values.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.minmax.assign( 3.14, -1.5, out, 1, 0 )\n [ -1.5, 3.14 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.max, base.min, base.minmaxabs","base.minmaxabs":"\nbase.minmaxabs( x, y )\n Returns the minimum and maximum absolute values.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum absolute values.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: Array\n Minimum and maximum absolute values.\n\n Examples\n --------\n > var v = base.minmaxabs( 3.14, 4.2 )\n [ 3.14, 4.2 ]\n > v = base.minmaxabs( -5.9, 3.14)\n [ 3.14, 5.9 ]\n > v = base.minmaxabs( 3.14, NaN )\n [ NaN, NaN ]\n > v = base.minmaxabs( +0.0, -0.0 )\n [ 0.0, 0.0 ]\n\n\nbase.minmaxabs.assign( x, y, out, stride, offset )\n Returns the minimum and maximum absolute values.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum absolute values.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n out: Array|TypedArray|Object\n Output object.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Minimum and maximum absolute values.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.minmaxabs.assign( 3.14, -3.14, out, 1, 0 )\n [ 3.14, 3.14 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.maxabs, base.minabs, base.minmax\n","base.minmaxabs.assign":"\nbase.minmaxabs.assign( x, y, out, stride, offset )\n Returns the minimum and maximum absolute values.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum absolute values.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n out: Array|TypedArray|Object\n Output object.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Minimum and maximum absolute values.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.minmaxabs.assign( 3.14, -3.14, out, 1, 0 )\n [ 3.14, 3.14 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.maxabs, base.minabs, base.minmax","base.minmaxabsn":"\nbase.minmaxabsn( [x[, y[, ...args]]] )\n Returns the minimum and maximum absolute values.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum absolute values.\n\n When an empty set is considered a subset of the extended reals (all real\n numbers, including positive and negative infinity), positive infinity is the\n greatest lower bound and negative infinity is the least upper bound. Similar\n to zero being the identity element for the sum of an empty set and to one\n being the identity element for the product of an empty set, positive\n infinity is the identity element for the minimum and negative infinity is\n the identity element for the maximum, and thus, if not provided any\n arguments, the function returns positive infinity for both the minimum and\n maximum absolute values.\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n Returns\n -------\n out: Array\n Minimum and maximum absolute values.\n\n Examples\n --------\n > var v = base.minmaxabsn( 3.14, 4.2 )\n [ 3.14, 4.2 ]\n > v = base.minmaxabsn( -5.9, 3.14, 4.2 )\n [ 3.14, 5.9 ]\n > v = base.minmaxabsn( 3.14, NaN )\n [ NaN, NaN ]\n > v = base.minmaxabsn( +0.0, -0.0 )\n [ 0.0, 0.0 ]\n > v = base.minmaxabsn( 3.14 )\n [ 3.14, 3.14 ]\n\n\nbase.minmaxabsn.assign( [x[, y[, ...args]]], out, stride, offset )\n Returns the minimum and maximum absolute values.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum absolute values.\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n out: Array|TypedArray|Object\n Output object.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Minimum and maximum absolute values.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.minmaxabsn.assign( 3.14, out, 1, 0 )\n [ 3.14, 3.14 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.maxabsn, base.minabsn, base.minmaxn\n","base.minmaxabsn.assign":"\nbase.minmaxabsn.assign( [x[, y[, ...args]]], out, stride, offset )\n Returns the minimum and maximum absolute values.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum absolute values.\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n out: Array|TypedArray|Object\n Output object.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Minimum and maximum absolute values.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.minmaxabsn.assign( 3.14, out, 1, 0 )\n [ 3.14, 3.14 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.maxabsn, base.minabsn, base.minmaxn","base.minmaxn":"\nbase.minmaxn( [x[, y[, ...args]]] )\n Returns the minimum and maximum values.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum values.\n\n When an empty set is considered a subset of the extended reals (all real\n numbers, including positive and negative infinity), positive infinity is the\n greatest lower bound and negative infinity is the least upper bound. Similar\n to zero being the identity element for the sum of an empty set and to one\n being the identity element for the product of an empty set, positive\n infinity is the identity element for the minimum and negative infinity is\n the identity element for the maximum, and thus, if not provided any\n arguments, the function returns positive infinity for the minimum value and\n negative infinity for the maximum value.\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n Returns\n -------\n out: Array\n Minimum and maximum values.\n\n Examples\n --------\n > var v = base.minmaxn( 3.14, 4.2 )\n [ 3.14, 4.2 ]\n > v = base.minmaxn( 5.9, 3.14, 4.2 )\n [ 3.14, 5.9 ]\n > v = base.minmaxn( 3.14, NaN )\n [ NaN, NaN ]\n > v = base.minmaxn( +0.0, -0.0 )\n [ -0.0, +0.0 ]\n > v = base.minmaxn( 3.14 )\n [ 3.14, 3.14 ]\n\n\nbase.minmaxn.assign( [x[, y[, ...args]]], out, stride, offset )\n Returns the minimum and maximum values and assigns results to a provided\n output array.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum values.\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n out: Array|TypedArray|Object\n Output object.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Minimum and maximum values.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.minmaxn.assign( 3.14, -1.5, out, 1, 0 )\n [ -1.5, 3.14 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.maxn, base.minn, base.minmaxabsn","base.minmaxn.assign":"\nbase.minmaxn.assign( [x[, y[, ...args]]], out, stride, offset )\n Returns the minimum and maximum values and assigns results to a provided\n output array.\n\n If any argument is `NaN`, the function returns `NaN` for both the minimum\n and maximum values.\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n out: Array|TypedArray|Object\n Output object.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Minimum and maximum values.\n\n Examples\n --------\n > var out = [ 0.0, 0.0 ];\n > var v = base.minmaxn.assign( 3.14, -1.5, out, 1, 0 )\n [ -1.5, 3.14 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.maxn, base.minn, base.minmaxabsn","base.minn":"\nbase.minn( [x[, y[, ...args]]] )\n Returns the minimum value.\n\n If any argument is `NaN`, the function returns `NaN`.\n\n When an empty set is considered a subset of the extended reals (all real\n numbers, including positive and negative infinity), positive infinity is the\n greatest lower bound. Similar to zero being the identity element for the sum\n of an empty set and to one being the identity element for the product of an\n empty set, positive infinity is the identity element for the minimum, and\n thus, if not provided any arguments, the function returns positive infinity.\n\n Parameters\n ----------\n x: number (optional)\n First number.\n\n y: number (optional)\n Second number.\n\n args: ...number (optional)\n Numbers.\n\n Returns\n -------\n out: number\n Minimum value.\n\n Examples\n --------\n > var v = base.minn( 3.14, 4.2 )\n 3.14\n > v = base.minn( 5.9, 3.14, 4.2 )\n 3.14\n > v = base.minn( 3.14, NaN )\n NaN\n > v = base.minn( +0.0, -0.0 )\n -0.0\n\n See Also\n --------\n base.maxn, base.min, base.minabsn\n","base.modf":"\nbase.modf( x )\n Decomposes a double-precision floating-point number into integral and\n fractional parts, each having the same type and sign as the input value.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n out: Array\n Integral and fractional parts.\n\n Examples\n --------\n > var parts = base.modf( 3.14 )\n [ 3.0, 0.14000000000000012 ]\n > parts = base.modf( 3.14 )\n [ 3.0, 0.14000000000000012 ]\n > parts = base.modf( +0.0 )\n [ +0.0, +0.0 ]\n > parts = base.modf( -0.0 )\n [ -0.0, -0.0 ]\n > parts = base.modf( PINF )\n [ Infinity, +0.0 ]\n > parts = base.modf( NINF )\n [ -Infinity, -0.0 ]\n > parts = base.modf( NaN )\n [ NaN, NaN ]\n\n\nbase.modf.assign( x, out, stride, offset )\n Decomposes a double-precision floating-point number into integral and\n fractional parts, each having the same type and sign as the input value,\n and assigns results to a provided output array.\n\n Parameters\n ----------\n x: number\n Input value.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Integral and fractional parts.\n\n Examples\n --------\n > var out = new Float64Array( 2 );\n > var parts = base.modf.assign( 3.14, out, 1, 0 )\n [ 3.0, 0.14000000000000012 ]\n > var bool = ( parts === out )\n true\n","base.modf.assign":"\nbase.modf.assign( x, out, stride, offset )\n Decomposes a double-precision floating-point number into integral and\n fractional parts, each having the same type and sign as the input value,\n and assigns results to a provided output array.\n\n Parameters\n ----------\n x: number\n Input value.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Integral and fractional parts.\n\n Examples\n --------\n > var out = new Float64Array( 2 );\n > var parts = base.modf.assign( 3.14, out, 1, 0 )\n [ 3.0, 0.14000000000000012 ]\n > var bool = ( parts === out )\n true","base.mul":"\nbase.mul( x, y )\n Multiplies two double-precision floating-point numbers `x` and `y`.\n\n Parameters\n ----------\n x: number\n First input value.\n\n y: number\n Second input value.\n\n Returns\n -------\n z: number\n Result.\n\n Examples\n --------\n > var v = base.mul( -1.0, 5.0 )\n -5.0\n > v = base.mul( 2.0, 5.0 )\n 10.0\n > v = base.mul( 0.0, 5.0 )\n 0.0\n > v = base.mul( -0.0, 0.0 )\n -0.0\n > v = base.mul( NaN, NaN )\n NaN\n\n See Also\n --------\n base.add, base.div, base.sub\n","base.mulf":"\nbase.mulf( x, y )\n Multiplies two single-precision floating-point numbers `x` and `y`.\n\n Parameters\n ----------\n x: number\n First input value.\n\n y: number\n Second input value.\n\n Returns\n -------\n z: number\n Result.\n\n Examples\n --------\n > var v = base.mulf( -1.0, 5.0 )\n -5.0\n > v = base.mulf( 2.0, 5.0 )\n 10.0\n > v = base.mulf( 0.0, 5.0 )\n 0.0\n > v = base.mulf( -0.0, 0.0 )\n -0.0\n > v = base.mulf( NaN, NaN )\n NaN\n\n See Also\n --------\n base.addf, base.divf, base.mul, base.subf\n","base.ndarray":"\nbase.ndarray( dtype, buffer, shape, strides, offset, order )\n Returns an ndarray.\n\n Parameters\n ----------\n dtype: string\n Underlying data type.\n\n buffer: ArrayLikeObject|TypedArray|Buffer\n Data buffer. A data buffer must be an array-like object (i.e., have a\n `length` property). For data buffers which are not indexed collections\n (i.e., collections which cannot support direct index access, such as\n `buffer[ index ]`; e.g., Complex64Array, Complex128Array, etc), a data\n buffer should provide `#.get( idx )` and `#.set( v[, idx] )` methods.\n Note that, for `set` methods, the value to set should be the first\n argument, followed by the linear index, similar to the native typed\n array `set` method.\n\n shape: ArrayLikeObject\n Array shape.\n\n strides: ArrayLikeObject\n Array strides.\n\n offset: integer\n Index offset.\n\n order: string\n Specifies whether an array is row-major (C-style) or column-major\n (Fortran-style).\n\n Returns\n -------\n ndarray: ndarray\n ndarray instance.\n\n Examples\n --------\n // Create a new instance...\n > var b = [ 1, 2, 3, 4 ]; // underlying data buffer\n > var d = [ 2, 2 ]; // shape\n > var s = [ 2, 1 ]; // strides\n > var o = 0; // index offset\n > var arr = base.ndarray( 'generic', b, d, s, o, 'row-major' )\n \n\n // Get an element using subscripts:\n > var v = arr.get( 1, 1 )\n 4\n\n // Get an element using a linear index:\n > v = arr.iget( 3 )\n 4\n\n // Set an element using subscripts:\n > arr.set( 1, 1, 40 );\n > arr.get( 1, 1 )\n 40\n\n // Set an element using a linear index:\n > arr.iset( 3, 99 );\n > arr.get( 1, 1 )\n 99\n\n\nbase.ndarray.prototype.byteLength\n Size (in bytes) of the array (if known).\n\n Returns\n -------\n size: integer|null\n Size (in bytes) of the array.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var sz = arr.byteLength\n 32\n\n\nbase.ndarray.prototype.BYTES_PER_ELEMENT\n Size (in bytes) of each array element (if known).\n\n Returns\n -------\n size: integer|null\n Size (in bytes) of each array element.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var sz = arr.BYTES_PER_ELEMENT\n 8\n\n\nbase.ndarray.prototype.data\n Pointer to the underlying data buffer.\n\n Returns\n -------\n buf: ArrayLikeObject|TypedArray|Buffer\n Underlying data buffer.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var buf = arr.data\n [ 1.0, 2.0, 3.0, 4.0 ]\n\n\nbase.ndarray.prototype.dtype\n Underlying data type.\n\n Returns\n -------\n dtype: string\n Underlying data type.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var dt = arr.dtype\n 'float64'\n\n\nbase.ndarray.prototype.flags\n Meta information, such as information concerning the memory layout of the\n array.\n\n Returns\n -------\n flags: Object\n Info object.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var fl = arr.flags\n {...}\n\n\nbase.ndarray.prototype.length\n Length of the array (i.e., number of elements).\n\n Returns\n -------\n len: integer\n Array length.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var len = arr.length\n 4\n\n\nbase.ndarray.prototype.ndims\n Number of dimensions.\n\n Returns\n -------\n ndims: integer\n Number of dimensions.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var n = arr.ndims\n 2\n\n\nbase.ndarray.prototype.offset\n Index offset which specifies the buffer index at which to start iterating\n over array elements.\n\n Returns\n -------\n offset: integer\n Index offset.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var v = arr.offset\n 0\n\n\nbase.ndarray.prototype.order: string\n Array order.\n\n The array order is either row-major (C-style) or column-major (Fortran-\n style).\n\n Returns\n -------\n order: string\n Array order.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var ord = arr.order\n 'row-major'\n\n\nbase.ndarray.prototype.shape\n Array shape.\n\n Returns\n -------\n shape: Array\n Array shape.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var sh = arr.shape\n [ 2, 2 ]\n\n\nbase.ndarray.prototype.strides\n Index strides which specify how to access data along corresponding array\n dimensions.\n\n Returns\n -------\n strides: Array\n Index strides.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var st = arr.strides\n [ 2, 1 ]\n\n\nbase.ndarray.prototype.get( ...idx )\n Returns an array element specified according to provided subscripts.\n\n The number of provided subscripts should equal the number of dimensions.\n\n For zero-dimensional arrays, no indices should be provided.\n\n Parameters\n ----------\n idx: ...integer\n Subscripts.\n\n Returns\n -------\n out: any\n Array element.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var v = arr.get( 1, 1 )\n 4.0\n\n\nbase.ndarray.prototype.iget( idx )\n Returns an array element located at a specified linear index.\n\n For zero-dimensional arrays, the input argument is ignored and, for clarity,\n should not be provided.\n\n Parameters\n ----------\n idx: integer\n Linear index.\n\n Returns\n -------\n out: any\n Array element.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var v = arr.iget( 3 )\n 4.0\n\n\nbase.ndarray.prototype.set( ...idx, v )\n Sets an array element specified according to provided subscripts.\n\n The number of provided subscripts should equal the number of dimensions.\n\n For zero-dimensional arrays, no indices should be provided.\n\n Parameters\n ----------\n idx: ...integer\n Subscripts.\n\n v: any\n Value to set.\n\n Returns\n -------\n out: ndarray\n ndarray instance.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > arr.set( 1, 1, -4.0 );\n > arr.get( 1, 1 )\n -4.0\n\n\nbase.ndarray.prototype.iset( idx, v )\n Sets an array element located at a specified linear index.\n\n For zero-dimensional arrays, the first, and only, argument should be the\n value to set.\n\n Parameters\n ----------\n idx: integer\n Linear index.\n\n v: any\n Value to set.\n\n Returns\n -------\n out: ndarray\n ndarray instance.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > arr.iset( 3, -4.0 );\n > arr.iget( 3 )\n -4.0\n\n\nbase.ndarray.prototype.toString()\n Serializes an ndarray as a string.\n\n This method does **not** serialize data outside of the buffer region defined\n by the array configuration.\n\n Returns\n -------\n str: string\n Serialized ndarray string.\n\n Examples\n --------\n > var b = [ 1, 2, 3, 4 ];\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'generic', b, d, s, o, 'row-major' );\n > arr.toString()\n '...'\n\n\nbase.ndarray.prototype.toJSON()\n Serializes an ndarray as a JSON object.\n\n This method does **not** serialize data outside of the buffer region defined\n by the array configuration.\n\n Returns\n -------\n obj: Object\n JSON object.\n\n Examples\n --------\n > var b = [ 1, 2, 3, 4 ];\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'generic', b, d, s, o, 'row-major' );\n > arr.toJSON()\n {...}\n\n See Also\n --------\n array, ndarray\n","base.ndarray.prototype.byteLength":"\nbase.ndarray.prototype.byteLength\n Size (in bytes) of the array (if known).\n\n Returns\n -------\n size: integer|null\n Size (in bytes) of the array.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var sz = arr.byteLength\n 32","base.ndarray.prototype.BYTES_PER_ELEMENT":"\nbase.ndarray.prototype.BYTES_PER_ELEMENT\n Size (in bytes) of each array element (if known).\n\n Returns\n -------\n size: integer|null\n Size (in bytes) of each array element.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var sz = arr.BYTES_PER_ELEMENT\n 8","base.ndarray.prototype.data":"\nbase.ndarray.prototype.data\n Pointer to the underlying data buffer.\n\n Returns\n -------\n buf: ArrayLikeObject|TypedArray|Buffer\n Underlying data buffer.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var buf = arr.data\n [ 1.0, 2.0, 3.0, 4.0 ]","base.ndarray.prototype.dtype":"\nbase.ndarray.prototype.dtype\n Underlying data type.\n\n Returns\n -------\n dtype: string\n Underlying data type.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var dt = arr.dtype\n 'float64'","base.ndarray.prototype.flags":"\nbase.ndarray.prototype.flags\n Meta information, such as information concerning the memory layout of the\n array.\n\n Returns\n -------\n flags: Object\n Info object.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var fl = arr.flags\n {...}","base.ndarray.prototype.length":"\nbase.ndarray.prototype.length\n Length of the array (i.e., number of elements).\n\n Returns\n -------\n len: integer\n Array length.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var len = arr.length\n 4","base.ndarray.prototype.ndims":"\nbase.ndarray.prototype.ndims\n Number of dimensions.\n\n Returns\n -------\n ndims: integer\n Number of dimensions.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var n = arr.ndims\n 2","base.ndarray.prototype.offset":"\nbase.ndarray.prototype.offset\n Index offset which specifies the buffer index at which to start iterating\n over array elements.\n\n Returns\n -------\n offset: integer\n Index offset.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var v = arr.offset\n 0","base.ndarray.prototype.order: string":"\nbase.ndarray.prototype.order: string\n Array order.\n\n The array order is either row-major (C-style) or column-major (Fortran-\n style).\n\n Returns\n -------\n order: string\n Array order.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var ord = arr.order\n 'row-major'","base.ndarray.prototype.shape":"\nbase.ndarray.prototype.shape\n Array shape.\n\n Returns\n -------\n shape: Array\n Array shape.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var sh = arr.shape\n [ 2, 2 ]","base.ndarray.prototype.strides":"\nbase.ndarray.prototype.strides\n Index strides which specify how to access data along corresponding array\n dimensions.\n\n Returns\n -------\n strides: Array\n Index strides.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var st = arr.strides\n [ 2, 1 ]","base.ndarray.prototype.get":"\nbase.ndarray.prototype.get( ...idx )\n Returns an array element specified according to provided subscripts.\n\n The number of provided subscripts should equal the number of dimensions.\n\n For zero-dimensional arrays, no indices should be provided.\n\n Parameters\n ----------\n idx: ...integer\n Subscripts.\n\n Returns\n -------\n out: any\n Array element.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var v = arr.get( 1, 1 )\n 4.0","base.ndarray.prototype.iget":"\nbase.ndarray.prototype.iget( idx )\n Returns an array element located at a specified linear index.\n\n For zero-dimensional arrays, the input argument is ignored and, for clarity,\n should not be provided.\n\n Parameters\n ----------\n idx: integer\n Linear index.\n\n Returns\n -------\n out: any\n Array element.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > var v = arr.iget( 3 )\n 4.0","base.ndarray.prototype.set":"\nbase.ndarray.prototype.set( ...idx, v )\n Sets an array element specified according to provided subscripts.\n\n The number of provided subscripts should equal the number of dimensions.\n\n For zero-dimensional arrays, no indices should be provided.\n\n Parameters\n ----------\n idx: ...integer\n Subscripts.\n\n v: any\n Value to set.\n\n Returns\n -------\n out: ndarray\n ndarray instance.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > arr.set( 1, 1, -4.0 );\n > arr.get( 1, 1 )\n -4.0","base.ndarray.prototype.iset":"\nbase.ndarray.prototype.iset( idx, v )\n Sets an array element located at a specified linear index.\n\n For zero-dimensional arrays, the first, and only, argument should be the\n value to set.\n\n Parameters\n ----------\n idx: integer\n Linear index.\n\n v: any\n Value to set.\n\n Returns\n -------\n out: ndarray\n ndarray instance.\n\n Examples\n --------\n > var b = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'float64', b, d, s, o, 'row-major' );\n > arr.iset( 3, -4.0 );\n > arr.iget( 3 )\n -4.0","base.ndarray.prototype.toString":"\nbase.ndarray.prototype.toString()\n Serializes an ndarray as a string.\n\n This method does **not** serialize data outside of the buffer region defined\n by the array configuration.\n\n Returns\n -------\n str: string\n Serialized ndarray string.\n\n Examples\n --------\n > var b = [ 1, 2, 3, 4 ];\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'generic', b, d, s, o, 'row-major' );\n > arr.toString()\n '...'","base.ndarray.prototype.toJSON":"\nbase.ndarray.prototype.toJSON()\n Serializes an ndarray as a JSON object.\n\n This method does **not** serialize data outside of the buffer region defined\n by the array configuration.\n\n Returns\n -------\n obj: Object\n JSON object.\n\n Examples\n --------\n > var b = [ 1, 2, 3, 4 ];\n > var d = [ 2, 2 ];\n > var s = [ 2, 1 ];\n > var o = 0;\n > var arr = base.ndarray( 'generic', b, d, s, o, 'row-major' );\n > arr.toJSON()\n {...}\n\n See Also\n --------\n array, ndarray","base.ndarrayUnary":"\nbase.ndarrayUnary( arrays, fcn )\n Applies a unary callback to elements in an input ndarray and assigns results\n to elements in an output ndarray.\n\n Each provided \"ndarray\" should be an object with the following properties:\n\n - dtype: data type.\n - data: data buffer.\n - shape: dimensions.\n - strides: stride lengths.\n - offset: index offset.\n - order: specifies whether an ndarray is row-major (C-style) or column-major\n (Fortran-style).\n\n Parameters\n ----------\n arrays: ArrayLikeObject\n Array-like object containing one input ndarray and one output ndarray.\n\n fcn: Function\n Unary callback.\n\n Examples\n --------\n // Define ndarray data and meta data...\n > var xbuf = new Float64Array( [ -1.0, -2.0, -3.0, -4.0 ] );\n > var ybuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );\n > var dtype = 'float64';\n > var shape = [ 2, 2 ];\n > var sx = [ 2, 1 ];\n > var sy = [ 2, 1 ];\n > var ox = 0;\n > var oy = 0;\n > var order = 'row-major';\n\n // Using ndarrays...\n > var x = ndarray( dtype, xbuf, shape, sx, ox, order );\n > var y = ndarray( dtype, ybuf, shape, sy, oy, order );\n > base.ndarrayUnary( [ x, y ], base.abs );\n > y.data\n [ 1.0, 2.0, 3.0, 4.0 ]\n\n // Using minimal ndarray-like objects...\n > x = {\n ... 'dtype': dtype,\n ... 'data': xbuf,\n ... 'shape': shape,\n ... 'strides': sx,\n ... 'offset': ox,\n ... 'order': order\n ... };\n > y = {\n ... 'dtype': dtype,\n ... 'data': ybuf,\n ... 'shape': shape,\n ... 'strides': sy,\n ... 'offset': oy,\n ... 'order': order\n ... };\n > base.ndarrayUnary( [ x, y ], base.abs );\n > y.data\n [ 1.0, 2.0, 3.0, 4.0 ]\n\n See Also\n --------\n ndarrayDispatch\n","base.ndzeros":"\nbase.ndzeros( dtype, shape, order )\n Returns a zero-filled ndarray having a specified shape and data type.\n\n Parameters\n ----------\n dtype: string\n Underlying data type. Must be a numeric data type or \"generic\".\n\n shape: ArrayLikeObject\n Array shape.\n\n order: string\n Specifies whether an array is row-major (C-style) or column-major\n (Fortran-style).\n\n Returns\n -------\n out: ndarray\n Output array.\n\n Examples\n --------\n > var arr = base.ndzeros( 'float64', [ 2, 2 ], 'row-major' )\n \n > var sh = arr.shape\n [ 2, 2 ]\n > var dt = arr.dtype\n 'float64'\n\n See Also\n --------\n base.ndarray, base.ndzerosLike\n","base.ndzerosLike":"\nbase.ndzerosLike( x )\n Returns a zero-filled ndarray having the same shape and data type as a\n provided input ndarray.\n\n Along with data type, shape, and order, the function infers the \"class\" of\n the returned ndarray from the provided ndarray. For example, if provided a\n \"base\" ndarray, the function returns a base ndarray. If provided a non-base\n ndarray, the function returns a non-base ndarray.\n\n Parameters\n ----------\n x: ndarray\n Input array.\n\n Returns\n -------\n out: ndarray\n Output array.\n\n Examples\n --------\n > var x = base.ndzeros( 'float64', [ 2, 2 ], 'row-major' )\n \n > var sh = x.shape\n [ 2, 2 ]\n > var dt = x.dtype\n 'float64'\n > var y = base.ndzerosLike( x )\n \n > sh = y.shape\n [ 2, 2 ]\n > dt = y.dtype\n 'float64'\n\n See Also\n --------\n base.ndarray, base.ndzeros\n","base.negafibonacci":"\nbase.negafibonacci( n )\n Computes the nth negaFibonacci number.\n\n The negaFibonacci numbers follow the recurrence relation\n\n F_{n-2} = F_{n} - F_{n-1}\n\n with seed values F_0 = 0 and F_{-1} = 1.\n\n If `|n|` is greater than `78`, the function returns `NaN` as larger\n negaFibonacci numbers cannot be accurately represented due to limitations of\n double-precision floating-point format.\n\n If not provided a non-positive integer value, the function returns `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Input value.\n\n Returns\n -------\n y: integer\n NegaFibonacci number.\n\n Examples\n --------\n > var y = base.negafibonacci( 0 )\n 0\n > y = base.negafibonacci( -1 )\n 1\n > y = base.negafibonacci( -2 )\n -1\n > y = base.negafibonacci( -3 )\n 2\n > y = base.negafibonacci( -4 )\n -3\n > y = base.negafibonacci( -79 )\n NaN\n > y = base.negafibonacci( -80 )\n NaN\n > y = base.negafibonacci( NaN )\n NaN\n\n See Also\n --------\n base.fibonacci, base.negalucas\n","base.negalucas":"\nbase.negalucas( n )\n Computes the nth negaLucas number.\n\n The negaLucas numbers follow the recurrence relation\n\n L_{n-2} = L_{n} - L_{n-1}\n\n with seed values L_0 = 2 and L_{-1} = -1.\n\n If `|n|` is greater than `76`, the function returns `NaN` as larger\n negaLucas numbers cannot be accurately represented due to limitations of\n double-precision floating-point format.\n\n If not provided a non-positive integer value, the function returns `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Input value.\n\n Returns\n -------\n y: integer\n NegaLucas number.\n\n Examples\n --------\n > var y = base.negalucas( 0 )\n 2\n > y = base.negalucas( -1 )\n -1\n > y = base.negalucas( -2 )\n 3\n > y = base.negalucas( -3 )\n -4\n > y = base.negalucas( -4 )\n 7\n > y = base.negalucas( -77 )\n NaN\n > y = base.negalucas( -78 )\n NaN\n > y = base.negalucas( NaN )\n NaN\n\n See Also\n --------\n base.fibonacci, base.lucas, base.negafibonacci\n","base.nonfibonacci":"\nbase.nonfibonacci( n )\n Computes the nth non-Fibonacci number.\n\n If not provided a nonnegative integer value, the function returns `NaN`.\n\n If provided `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Input value.\n\n Returns\n -------\n y: number\n Non-Fibonacci number.\n\n Examples\n --------\n > var v = base.nonfibonacci( 1 )\n 4\n > v = base.nonfibonacci( 2 )\n 6\n > v = base.nonfibonacci( 3 )\n 7\n > v = base.nonfibonacci( NaN )\n NaN\n\n See Also\n --------\n base.fibonacci\n","base.normalize":"\nbase.normalize( x )\n Returns a normal number and exponent satisfying `x = y * 2^exp` as an array.\n\n The first element of the returned array corresponds to `y` and the second to\n `exp`.\n\n Parameters\n ----------\n x: number\n Double-precision floating-point number.\n\n Returns\n -------\n out: Array\n An array containing `y` and `exp`.\n\n Examples\n --------\n > var out = base.normalize( 3.14e-319 )\n [ 1.4141234400356668e-303, -52 ]\n > var y = out[ 0 ];\n > var exponent = out[ 1 ];\n > var bool = ( y*base.pow(2.0, exponent) === 3.14e-319 )\n true\n\n // Special cases:\n > out = base.normalize( 0.0 )\n [ 0.0, 0 ];\n > out = base.normalize( PINF )\n [ Infinity, 0 ]\n > out = base.normalize( NINF )\n [ -Infinity, 0 ]\n > out = base.normalize( NaN )\n [ NaN, 0 ]\n\n\nbase.normalize.assign( x, out, stride, offset )\n Returns a normal number and exponent satisfying `x = y * 2^exp` and assigns\n results to a provided output array.\n\n The first element of the returned array corresponds to `y` and the second to\n `exp`.\n\n Parameters\n ----------\n x: number\n Double-precision floating-point number.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array.\n\n Examples\n --------\n > var out = new Float64Array( 2 )\n > var v = base.normalize.assign( 3.14e-319, out, 1, 0 )\n [ 1.4141234400356668e-303, -52 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.normalizef\n","base.normalize.assign":"\nbase.normalize.assign( x, out, stride, offset )\n Returns a normal number and exponent satisfying `x = y * 2^exp` and assigns\n results to a provided output array.\n\n The first element of the returned array corresponds to `y` and the second to\n `exp`.\n\n Parameters\n ----------\n x: number\n Double-precision floating-point number.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n Output array.\n\n Examples\n --------\n > var out = new Float64Array( 2 )\n > var v = base.normalize.assign( 3.14e-319, out, 1, 0 )\n [ 1.4141234400356668e-303, -52 ]\n > var bool = ( v === out )\n true\n\n See Also\n --------\n base.normalizef","base.normalizef":"\nbase.normalizef( x )\n Returns a normal number `y` and exponent `exp` satisfying `x = y * 2^exp` as\n an array.\n\n The first element of the returned array corresponds to `y` and the second to\n `exp`.\n\n While the function accepts higher precision floating-point numbers, beware\n that providing such numbers can be a source of subtle bugs as the relation\n `x = y * 2^exp` may not hold.\n\n Parameters\n ----------\n x: float\n Single-precision floating-point number.\n\n Returns\n -------\n out: Array\n An array containing `y` and `exp`.\n\n Examples\n --------\n > var out = base.normalizef( base.float64ToFloat32( 1.401e-45 ) )\n [ 1.1754943508222875e-38, -23 ]\n > var y = out[ 0 ];\n > var exp = out[ 1 ];\n > var bool = ( y*base.pow(2,exp) === base.float64ToFloat32(1.401e-45) )\n true\n\n // Special cases:\n > out = base.normalizef( FLOAT32_PINF )\n [ Infinity, 0 ]\n > out = base.normalizef( FLOAT32_NINF )\n [ -Infinity, 0 ]\n > out = base.normalizef( NaN )\n [ NaN, 0 ]\n\n\nbase.normalizef.assign( x, out, stride, offset )\n Returns a normal number `y` and exponent `exp` satisfying `x = y * 2^exp` and\n assigns results to a provided output array.\n\n The first element of the returned array corresponds to `y` and the second to\n `exp`.\n\n While the function accepts higher precision floating-point numbers, beware\n that providing such numbers can be a source of subtle bugs as the relation\n `x = y * 2^exp` may not hold.\n\n Parameters\n ----------\n x: float\n Single-precision floating-point number.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n An array containing `y` and `exp`.\n\n Examples\n --------\n > out = new Float32Array( 2 );\n > var v = base.normalizef.assign( base.float64ToFloat32( 1.401e-45 ), out, 1, 0 )\n [ 1.1754943508222875e-38, -23.0 ]\n > bool = ( v === out )\n true\n\n See Also\n --------\n base.normalize","base.normalizef.assign":"\nbase.normalizef.assign( x, out, stride, offset )\n Returns a normal number `y` and exponent `exp` satisfying `x = y * 2^exp` and\n assigns results to a provided output array.\n\n The first element of the returned array corresponds to `y` and the second to\n `exp`.\n\n While the function accepts higher precision floating-point numbers, beware\n that providing such numbers can be a source of subtle bugs as the relation\n `x = y * 2^exp` may not hold.\n\n Parameters\n ----------\n x: float\n Single-precision floating-point number.\n\n out: Array|TypedArray|Object\n Output array.\n\n stride: integer\n Output array stride.\n\n offset: integer\n Output array index offset.\n\n Returns\n -------\n out: Array|TypedArray|Object\n An array containing `y` and `exp`.\n\n Examples\n --------\n > out = new Float32Array( 2 );\n > var v = base.normalizef.assign( base.float64ToFloat32( 1.401e-45 ), out, 1, 0 )\n [ 1.1754943508222875e-38, -23.0 ]\n > bool = ( v === out )\n true\n\n See Also\n --------\n base.normalize","base.normalizeSlice":"\nbase.normalizeSlice( slice, len, strict )\n Returns a normalized Slice object.\n\n In strict mode, the function returns an error object if an input slice\n exceeds index bounds.\n\n A returned error object is a plain object having the following properties:\n\n - code: error code.\n\n A returned error object may have one of the following error codes:\n\n - ERR_SLICE_OUT_OF_BOUNDS: a slice exceeds index bounds.\n\n Parameters\n ----------\n slice: Slice\n Input slice object.\n\n len: integer\n Maximum number of elements allowed in the slice.\n\n strict: boolean\n Boolean indicating whether to enforce strict bounds checking.\n\n Returns\n -------\n s: Slice|Object\n Slice instance (or an error object).\n\n Examples\n --------\n > var s1 = new Slice( 1, 10, 1 );\n > var s2 = base.normalizeSlice( s1, 5, false );\n > s2.start\n 1\n > s2.stop\n 5\n > s2.step\n 1\n > s1 = new Slice( -2, null, -1 );\n > s2 = base.normalizeSlice( s1, 10, false );\n > s2.start\n 8\n > s2.stop\n null\n > s2.step\n -1\n\n See Also\n --------\n base.normalizeMultiSlice\n","base.normhermitepoly":"\nbase.normhermitepoly( n, x )\n Evaluates a normalized Hermite polynomial using double-precision floating-\n point arithmetic.\n\n Parameters\n ----------\n n: integer\n Nonnegative polynomial degree.\n\n x: number\n Value at which to evaluate the polynomial.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.normhermitepoly( 1, 0.5 )\n 0.5\n > y = base.normhermitepoly( -1, 0.5 )\n NaN\n > y = base.normhermitepoly( 0, 0.5 )\n 1.0\n > y = base.normhermitepoly( 2, 0.5 )\n -0.75\n\n\nbase.normhermitepoly.factory( n )\n Returns a function for evaluating a normalized Hermite polynomial using\n double-precision floating-point arithmetic.\n\n Parameters\n ----------\n n: integer\n Nonnegative polynomial degree.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a normalized Hermite polynomial.\n\n Examples\n --------\n > var f = base.normhermitepoly.factory( 2 );\n > var v = f( 0.5 )\n -0.75\n\n See Also\n --------\n base.evalpoly, base.hermitepoly\n","base.normhermitepoly.factory":"\nbase.normhermitepoly.factory( n )\n Returns a function for evaluating a normalized Hermite polynomial using\n double-precision floating-point arithmetic.\n\n Parameters\n ----------\n n: integer\n Nonnegative polynomial degree.\n\n Returns\n -------\n fcn: Function\n Function for evaluating a normalized Hermite polynomial.\n\n Examples\n --------\n > var f = base.normhermitepoly.factory( 2 );\n > var v = f( 0.5 )\n -0.75\n\n See Also\n --------\n base.evalpoly, base.hermitepoly","base.pascalcase":"\nbase.pascalcase( str )\n Converts a string to Pascal case.\n\n Parameters\n ----------\n str: string\n Input string.\n\n Returns\n -------\n out: string\n Pascal-cased string.\n\n Examples\n --------\n > var out = base.pascalcase( 'Hello World!' )\n 'HelloWorld'\n > out = base.pascalcase( 'beep boop' )\n 'BeepBoop'\n\n See Also\n --------\n base.camelcase, base.lowercase, base.uppercase","base.pdiff":"\nbase.pdiff( x, y )\n Returns the positive difference between `x` and `y` if `x > y`; otherwise,\n returns `0`.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: number\n Positive difference.\n\n Examples\n --------\n > var v = base.pdiff( 5.9, 3.14 )\n 2.76\n > v = base.pdiff( 3.14, 4.2 )\n 0.0\n > v = base.pdiff( 3.14, NaN )\n NaN\n > v = base.pdiff( -0.0, +0.0 )\n +0.0\n\n","base.pdifff":"\nbase.pdifff( x, y )\n Returns the positive difference between `x` and `y` if `x > y`; otherwise,\n returns `0`.\n\n Parameters\n ----------\n x: number\n First number.\n\n y: number\n Second number.\n\n Returns\n -------\n out: number\n Positive difference.\n\n Examples\n --------\n > var v = base.pdifff( 5.9, 3.15 )\n 2.75\n > v = base.pdifff( 3.14, 4.2 )\n 0.0\n > v = base.pdifff( 3.14, NaN )\n NaN\n > v = base.pdifff( -0.0, +0.0 )\n +0.0\n\n See Also\n --------\n base.pdiff\n","base.percentEncode":"\nbase.percentEncode( str )\n Percent-encodes a UTF-16 encoded string according to RFC 3986.\n\n Parameters\n ----------\n str: string\n UTF-16 encoded string.\n\n Returns\n -------\n out: string\n Percent-encoded string.\n\n Examples\n --------\n > var out = base.percentEncode( '☃' )\n '%E2%98%83'\n\n","base.polygamma":"\nbase.polygamma( n, x )\n Evaluates the polygamma function of order `n`; i.e., the (n+1)th derivative\n of the natural logarithm of the gamma function.\n\n If `n` is not a nonnegative integer, the function returns `NaN`.\n\n If `x` is zero or a negative integer, the function returns `NaN`.\n\n If provided `NaN` as either parameter, the function returns `NaN`.\n\n Parameters\n ----------\n n: integer\n Derivative order.\n\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var v = base.polygamma( 3, 1.2 )\n ~3.245\n > v = base.polygamma( 5, 1.2 )\n ~41.39\n > v = base.polygamma( 3, -4.9 )\n ~60014.239\n > v = base.polygamma( -1, 5.3 )\n NaN\n > v = base.polygamma( 2, -1.0 )\n Infinity\n\n See Also\n --------\n base.trigamma, base.digamma, base.gamma\n","base.pow":"\nbase.pow( b, x )\n Evaluates the exponential function `bˣ`.\n\n Parameters\n ----------\n b: number\n Base.\n\n x: number\n Exponent.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.pow( 2.0, 3.0 )\n 8.0\n > y = base.pow( 4.0, 0.5 )\n 2.0\n > y = base.pow( 100.0, 0.0 )\n 1.0\n > y = base.pow( PI, 5.0 )\n ~306.0197\n > y = base.pow( PI, -0.2 )\n ~0.7954\n > y = base.pow( NaN, 3.0 )\n NaN\n > y = base.pow( 5.0, NaN )\n NaN\n > y = base.pow( NaN, NaN )\n NaN\n\n See Also\n --------\n base.exp, base.powm1\n","base.powm1":"\nbase.powm1( b, x )\n Evaluates `bˣ - 1`.\n\n When `b` is close to `1` and/or `x` is small, this function is more accurate\n than naively computing `bˣ` and subtracting `1`.\n\n Parameters\n ----------\n b: number\n Base.\n\n x: number\n Exponent.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.powm1( 2.0, 3.0 )\n 7.0\n > y = base.powm1( 4.0, 0.5 )\n 1.0\n > y = base.powm1( 0.0, 100.0 )\n -1.0\n > y = base.powm1( 100.0, 0.0 )\n 0.0\n > y = base.powm1( 0.0, 0.0 )\n 0.0\n > y = base.powm1( PI, 5.0 )\n ~305.0197\n > y = base.powm1( NaN, 3.0 )\n NaN\n > y = base.powm1( 5.0, NaN )\n NaN\n\n See Also\n --------\n base.pow\n","base.rad2deg":"\nbase.rad2deg( x )\n Converts an angle from radians to degrees.\n\n Parameters\n ----------\n x: number\n Angle in radians.\n\n Returns\n -------\n d: number\n Angle in degrees.\n\n Examples\n --------\n > var d = base.rad2deg( PI/2.0 )\n 90.0\n > d = base.rad2deg( -PI/4.0 )\n -45.0\n > d = base.rad2deg( NaN )\n NaN\n\n // Due to finite precision, canonical values may not be returned:\n > d = base.rad2deg( PI/6.0 )\n 29.999999999999996\n\n See Also\n --------\n base.deg2rad\n","base.rad2degf":"\nbase.rad2degf( x )\n Converts an angle from radians to degrees (single-precision).\n\n Parameters\n ----------\n x: number\n Angle in radians.\n\n Returns\n -------\n d: number\n Angle in degrees.\n\n Examples\n --------\n > var d = base.rad2degf( 3.141592653589793 / 2.0 )\n 90.0\n > d = base.rad2degf( -3.141592653589793 / 4.0 )\n -45.0\n > d = base.rad2degf( NaN )\n NaN\n\n // Due to finite precision, canonical values may not be returned:\n > d = base.rad2degf( 3.141592653589793 / 6.0 )\n 30.000001907348633\n\n See Also\n --------\n base.rad2deg\n","base.ramp":"\nbase.ramp( x )\n Evaluates the ramp function.\n\n If `x >= 0`, the function returns `x`; otherwise, the function returns zero.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.ramp( 3.14 )\n 3.14\n > y = base.ramp( -3.14 )\n 0.0\n\n See Also\n --------\n base.heaviside\n","base.rampf":"\nbase.rampf( x )\n Evaluates the ramp function (single-precision).\n\n If `x >= 0`, the function returns `x`; otherwise, the function returns zero.\n\n Parameters\n ----------\n x: number\n Input value.\n\n Returns\n -------\n y: number\n Function value.\n\n Examples\n --------\n > var y = base.rampf( 3.14 )\n 3.14\n > y = base.rampf( -3.14 )\n 0.0\n\n See Also\n --------\n base.ramp\n","base.random.arcsine":"\nbase.random.arcsine( a, b )\n Returns a pseudorandom number drawn from an arcsine distribution.\n\n If `a >= b`, the function returns `NaN`.\n\n If `a` or `b` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n a: number\n Minimum support.\n\n b: number\n Maximum support.\n\n Returns\n -------\n r: number\n Pseudorandom number.\n\n Examples\n --------\n > var r = base.random.arcsine( 2.0, 5.0 )\n \n\n\nbase.random.arcsine.factory( [a, b, ][options] )\n Returns a pseudorandom number generator (PRNG) for generating pseudorandom\n numbers drawn from an arcsine distribution.\n\n If provided `a` and `b`, the returned PRNG returns random variates drawn\n from the specified distribution.\n\n If not provided `a` and `b`, the returned PRNG requires that both `a` and\n `b` be provided at each invocation.\n\n Parameters\n ----------\n a: number (optional)\n Minimum support.\n\n b: number (optional)\n Maximum support.\n\n options: Object (optional)\n Options.\n\n options.prng: Function (optional)\n Pseudorandom number generator (PRNG) for generating uniformly\n distributed pseudorandom numbers on the interval `[0,1)`. If provided,\n the `state` and `seed` options are ignored. In order to seed the\n returned pseudorandom number generator, one must seed the provided\n `prng` (assuming the provided `prng` is seedable).\n\n options.seed: integer|ArrayLikeObject (optional)\n Pseudorandom number generator seed. The seed may be either a positive\n unsigned 32-bit integer or, for arbitrary length seeds, an array-like\n object containing unsigned 32-bit integers.\n\n options.state: Uint32Array (optional)\n Pseudorandom number generator state. If provided, the `seed` option is\n ignored.\n\n options.copy: boolean (optional)\n Boolean indicating whether to copy a provided pseudorandom number\n generator state. Setting this option to `false` allows sharing state\n between two or more pseudorandom number generators. Setting this option\n to `true` ensures that a returned generator has exclusive control over\n its internal state. Default: true.\n\n Returns\n -------\n rand: Function\n Pseudorandom number generator (PRNG).\n\n Examples\n --------\n // Basic usage:\n > var rand = base.random.arcsine.factory();\n > var r = rand( 0.0, 1.0 )\n \n > r = rand( -2.0, 2.0 )\n \n\n // Provide `a` and `b`:\n > rand = base.random.arcsine.factory( 0.0, 1.0 );\n > r = rand()\n \n > r = rand()\n \n\n\nbase.random.arcsine.NAME\n Generator name.\n\n Examples\n --------\n > var str = base.random.arcsine.NAME\n 'arcsine'\n\n\nbase.random.arcsine.PRNG\n Underlying pseudorandom number generator.\n\n Examples\n --------\n > var prng = base.random.arcsine.PRNG;\n\n\nbase.random.arcsine.seed\n Pseudorandom number generator seed.\n\n Examples\n --------\n > var seed = base.random.arcsine.seed;\n\n\nbase.random.arcsine.seedLength\n Length of generator seed.\n\n Examples\n --------\n > var len = base.random.arcsine.seedLength;\n\n\nbase.random.arcsine.state\n Generator state.\n\n Examples\n --------\n > var r = base.random.arcsine( 2.0, 4.0 )\n \n > r = base.random.arcsine( 2.0, 4.0 )\n \n > r = base.random.arcsine( 2.0, 4.0 )\n \n\n // Get a copy of the current state:\n > var state = base.random.arcsine.state\n \n\n > r = base.random.arcsine( 2.0, 4.0 )\n \n > r = base.random.arcsine( 2.0, 4.0 )\n \n\n // Set the state:\n > base.random.arcsine.state = state;\n\n // Replay the last two pseudorandom numbers:\n > r = base.random.arcsine( 2.0, 4.0 )\n \n > r = base.random.arcsine( 2.0, 4.0 )\n \n\n\nbase.random.arcsine.stateLength\n Length of generator state.\n\n Examples\n --------\n > var len = base.random.arcsine.stateLength;\n\n\nbase.random.arcsine.byteLength\n Size (in bytes) of generator state.\n\n Examples\n --------\n > var sz = base.random.arcsine.byteLength;\n\n\nbase.random.arcsine.toJSON()\n Serializes the pseudorandom number generator as a JSON object.\n\n Returns\n -------\n out: Object\n JSON representation.\n\n Examples\n --------\n > var o = base.random.arcsine.toJSON()\n { 'type': 'PRNG', 'name': '...', 'state': {...}, 'params': [] }\n\n See Also\n --------\n random.array.arcsine, random.iterators.arcsine, random.streams.arcsine, base.random.beta\n","base.random.arcsine.factory":"\nbase.random.arcsine.factory( [a, b, ][options] )\n Returns a pseudorandom number generator (PRNG) for generating pseudorandom\n numbers drawn from an arcsine distribution.\n\n If provided `a` and `b`, the returned PRNG returns random variates drawn\n from the specified distribution.\n\n If not provided `a` and `b`, the returned PRNG requires that both `a` and\n `b` be provided at each invocation.\n\n Parameters\n ----------\n a: number (optional)\n Minimum support.\n\n b: number (optional)\n Maximum support.\n\n options: Object (optional)\n Options.\n\n options.prng: Function (optional)\n Pseudorandom number generator (PRNG) for generating uniformly\n distributed pseudorandom numbers on the interval `[0,1)`. If provided,\n the `state` and `seed` options are ignored. In order to seed the\n returned pseudorandom number generator, one must seed the provided\n `prng` (assuming the provided `prng` is seedable).\n\n options.seed: integer|ArrayLikeObject (optional)\n Pseudorandom number generator seed. The seed may be either a positive\n unsigned 32-bit integer or, for arbitrary length seeds, an array-like\n object containing unsigned 32-bit integers.\n\n options.state: Uint32Array (optional)\n Pseudorandom number generator state. If provided, the `seed` option is\n ignored.\n\n options.copy: boolean (optional)\n Boolean indicating whether to copy a provided pseudorandom number\n generator state. Setting this option to `false` allows sharing state\n between two or more pseudorandom number generators. Setting this option\n to `true` ensures that a returned generator has exclusive control over\n its internal state. Default: true.\n\n Returns\n -------\n rand: Function\n Pseudorandom number generator (PRNG).\n\n Examples\n --------\n // Basic usage:\n > var rand = base.random.arcsine.factory();\n > var r = rand( 0.0, 1.0 )\n \n > r = rand( -2.0, 2.0 )\n \n\n // Provide `a` and `b`:\n > rand = base.random.arcsine.factory( 0.0, 1.0 );\n > r = rand()\n \n > r = rand()\n ","base.random.arcsine.NAME":"\nbase.random.arcsine.NAME\n Generator name.\n\n Examples\n --------\n > var str = base.random.arcsine.NAME\n 'arcsine'","base.random.arcsine.PRNG":"\nbase.random.arcsine.PRNG\n Underlying pseudorandom number generator.\n\n Examples\n --------\n > var prng = base.random.arcsine.PRNG;","base.random.arcsine.seed":"\nbase.random.arcsine.seed\n Pseudorandom number generator seed.\n\n Examples\n --------\n > var seed = base.random.arcsine.seed;","base.random.arcsine.seedLength":"\nbase.random.arcsine.seedLength\n Length of generator seed.\n\n Examples\n --------\n > var len = base.random.arcsine.seedLength;","base.random.arcsine.state":"\nbase.random.arcsine.state\n Generator state.\n\n Examples\n --------\n > var r = base.random.arcsine( 2.0, 4.0 )\n \n > r = base.random.arcsine( 2.0, 4.0 )\n \n > r = base.random.arcsine( 2.0, 4.0 )\n \n\n // Get a copy of the current state:\n > var state = base.random.arcsine.state\n \n\n > r = base.random.arcsine( 2.0, 4.0 )\n \n > r = base.random.arcsine( 2.0, 4.0 )\n \n\n // Set the state:\n > base.random.arcsine.state = state;\n\n // Replay the last two pseudorandom numbers:\n > r = base.random.arcsine( 2.0, 4.0 )\n \n > r = base.random.arcsine( 2.0, 4.0 )\n ","base.random.arcsine.stateLength":"\nbase.random.arcsine.stateLength\n Length of generator state.\n\n Examples\n --------\n > var len = base.random.arcsine.stateLength;","base.random.arcsine.byteLength":"\nbase.random.arcsine.byteLength\n Size (in bytes) of generator state.\n\n Examples\n --------\n > var sz = base.random.arcsine.byteLength;","base.random.arcsine.toJSON":"\nbase.random.arcsine.toJSON()\n Serializes the pseudorandom number generator as a JSON object.\n\n Returns\n -------\n out: Object\n JSON representation.\n\n Examples\n --------\n > var o = base.random.arcsine.toJSON()\n { 'type': 'PRNG', 'name': '...', 'state': {...}, 'params': [] }\n\n See Also\n --------\n random.array.arcsine, random.iterators.arcsine, random.streams.arcsine, base.random.beta","base.random.bernoulli":"\nbase.random.bernoulli( p )\n Returns a pseudorandom number drawn from a Bernoulli distribution.\n\n If `p < 0` or `p > 1` or `p` is `NaN`, the function returns `NaN`.\n\n Parameters\n ----------\n p: number\n Success probability.\n\n Returns\n -------\n r: integer\n Pseudorandom number.\n\n Examples\n --------\n > var r = base.random.bernoulli( 0.8 );\n\n\nbase.random.bernoulli.factory( [p, ][options] )\n Returns a pseudorandom number generator (PRNG) for generating pseudorandom\n numbers drawn from a Bernoulli distribution.\n\n If provided `p`, the returned PRNG returns random variates drawn from the\n specified distribution.\n\n If not provided `p`, the returned PRNG requires that `p` be provided at each\n invocation.\n\n Parameters\n ----------\n p: number (optional)\n Success probability.\n\n options: Object (optional)\n Options.\n\n options.prng: Function (optional)\n Pseudorandom number generator (PRNG) for generating uniformly\n distributed pseudorandom numbers on the interval `[0,1)`. If provided,\n the `state` and `seed` options are ignored. In order to seed the\n returned pseudorandom number generator, one must seed the provided\n `prng` (assuming the provided `prng` is seedable).\n\n options.seed: integer|ArrayLikeObject (optional)\n Pseudorandom number generator seed. The seed may be either a positive\n unsigned 32-bit integer or, for arbitrary length seeds, an array-like\n object containing unsigned 32-bit integers.\n\n options.state: Uint32Array (optional)\n Pseudorandom number generator state. If provided, the `seed` option is\n ignored.\n\n options.copy: boolean (optional)\n Boolean indicating whether to copy a provided pseudorandom number\n generator state. Setting this option to `false` allows sharing state\n between two or more pseudorandom number generators. Setting this option\n to `true` ensures that a returned generator has exclusive control over\n its internal state. Default: true.\n\n Returns\n -------\n rand: Function\n Pseudorandom number generator (PRNG).\n\n Examples\n --------\n // Basic usage:\n > var rand = base.random.bernoulli.factory();\n > var r = rand( 0.3 );\n > r = rand( 0.59 );\n\n // Provide `p`:\n > rand = base.random.bernoulli.factory( 0.3 );\n > r = rand();\n > r = rand();\n\n\nbase.random.bernoulli.NAME\n Generator name.\n\n Examples\n --------\n > var str = base.random.bernoulli.NAME\n 'bernoulli'\n\n\nbase.random.bernoulli.PRNG\n Underlying pseudorandom number generator.\n\n Examples\n --------\n > var prng = base.random.bernoulli.PRNG;\n\n\nbase.random.bernoulli.seed\n Pseudorandom number generator seed.\n\n Examples\n --------\n > var seed = base.random.bernoulli.seed;\n\n\nbase.random.bernoulli.seedLength\n Length of generator seed.\n\n Examples\n --------\n > var len = base.random.bernoulli.seedLength;\n\n\nbase.random.bernoulli.state\n Generator state.\n\n Examples\n --------\n > var r = base.random.bernoulli( 0.3 )\n \n > r = base.random.bernoulli( 0.3 )\n \n > r = base.random.bernoulli( 0.3 )\n \n\n // Get a copy of the current state:\n > var state = base.random.bernoulli.state\n \n\n > r = base.random.bernoulli( 0.3 )\n