lime (development version)

lime 0.5.3

explain() will now pass ... on to the relevant predict() method (#150)
explain.data.frame() gains a gower_pow argument to modify the calculated gower distance before use by raising it to the power of the given value (#158)
Fixed a bug when calculating R^2 on single feature explanations (@pkopper, #157)
Fixed formatting of text prediction html presentation (#145)
Fixed a bug when setting feature select method to "none" (#141)
Changes default colouring from green-red to blue-red (#137)
lime() now warns when quantile binning is not feasible and uses standard binning instead (#154)
Changed the lambda value in the local model fit to match the one used in the Python version according to the relationship given here: https://stats.stackexchange.com/a/270705
Added pkgdown site at https://lime.data-imaginist.com
Fixed a bug when using a proprocessor with data.frame explanations

Add build-in support for parsnip and ranger
Add preprocess argument to lime.data.frame to keep it in line with the other types. Use it to transform your data.frame into a new input that your model expects after permutations
magick is now only in suggest to cut down on heavy hard dependencies
explain now returns a tbl_df so you get pretty printing if you have tibble loaded
When plotting regression explanations of non-binned features the feature weight is now multiplied by its value
More consistent support for keras
Fix bug when xgboost was used with with default objective
Better errors when handling bad models
plot_features now has a cases argument for subsetting the data before plotting

Add support for image explanation. The dispatch will be on paths pointing to valid image files. Image explanations can be visualised using plot_image_explanation (#35)
Add support for neural networks from the keras package
Add as_classifier() and as_regressor() for ad-hoc specification of the model type in case the heuristic implemented in lime doesn't hold. as_classifier() also lets you add/overwrite the class labels.
Use gower as the new default similarity measure for tabular data
If bin_continuous = FALSE the default behavior is now to sample from a kernel density estimation rather than assume a normal distribution.
Fix bug when numeric features in the training data were constant (#56)
Fix bug when plotting regression explanations with plot_explanations() (#60)
Logical columns in tabular data is now supported (#75)
Overhaul of plot_text_explanation() with better formatting and scrolling support for many explanations
All plots now show the fit of the explainer so the user can assess the quality of the explanation

Added a NEWS.md file to track changes to the package.
Fixed bug when explaining regression models, due to drop=TRUE defaults (#33)
Integer features are no longer converted to numeric during permutations (#32)
Fix bug when working with xgboost and tabular predictions (@martinju #1)
Training data can now contain NA values (#8)
Keep ordering when plotting with plot_features() (#38)
Fix support for mlr by extracting predictions correctly
Added support for h2o (@mdancho84) (#40)
Throws meaningful error when all permutations have 0 similarity to original observation (#47)
Explaining data can now contain NA values (#45)
Support for Date and POSIXt columns. They will be kept constant during permutations so that lime will explain the model behaviour at the given timepoint based on the remaining features (#39).
Add plot_explanations() for an overview plot of a large explanation set