build based on 0e1eeab

dev/assets/Manifest.toml

@@ -482,9 +482,9 @@ version = "1.6.1"
 
 [[deps.DataStructures]]
 deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
-git-tree-sha1 = "3dbd312d370723b6bb43ba9d02fc36abade4518d"
+git-tree-sha1 = "ac67408d9ddf207de5cfa9a97e114352430f01ed"
 uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-version = "0.18.15"
+version = "0.18.16"
 
 [[deps.DataValueInterfaces]]
 git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6"
@@ -522,7 +522,7 @@ version = "6.108.0"
 deps = ["DiffEqBase", "DiffResults", "Distances", "Distributed", "Distributions", "DocStringExtensions", "DynamicHMC", "ForwardDiff", "LabelledArrays", "LinearAlgebra", "LogDensityProblemsAD", "MacroTools", "Missings", "ModelingToolkit", "Optim", "PDMats", "Parameters", "Random", "RecursiveArrayTools", "Reexport", "Requires", "StanSample", "StructArrays", "TransformVariables", "TransformedLogDensities", "Turing"]
 path = "/home/runner/work/DiffEqBayes.jl/DiffEqBayes.jl"
 uuid = "ebbdde9d-f333-5424-9be2-dbf1e9acfb5e"
-version = "3.6.0"
+version = "3.6.1"
 
 [[deps.DiffEqCallbacks]]
 deps = ["DataStructures", "DiffEqBase", "ForwardDiff", "Functors", "LinearAlgebra", "Markdown", "NLsolve", "Parameters", "RecipesBase", "RecursiveArrayTools", "SciMLBase", "StaticArraysCore"]

dev/index.html

@@ -3,7 +3,7 @@
 Pkg.add(&quot;DiffEqBayes&quot;)</code></pre><h2 id="Contributing"><a class="docs-heading-anchor" href="#Contributing">Contributing</a><a id="Contributing-1"></a><a class="docs-heading-anchor-permalink" href="#Contributing" title="Permalink"></a></h2><ul><li><p>Please refer to the <a href="https://github.com/SciML/ColPrac/blob/master/README.md">SciML ColPrac: Contributor&#39;s Guide on Collaborative Practices for Community Packages</a> for guidance on PRs, issues, and other matters relating to contributing to SciML.</p></li><li><p>See the <a href="https://github.com/SciML/SciMLStyle">SciML Style Guide</a> for common coding practices and other style decisions.</p></li><li><p>There are a few community forums:</p><ul><li>The #diffeq-bridged and #sciml-bridged channels in the <a href="https://julialang.org/slack/">Julia Slack</a></li><li>The #diffeq-bridged and #sciml-bridged channels in the <a href="https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged">Julia Zulip</a></li><li>On the <a href="https://discourse.julialang.org">Julia Discourse forums</a></li><li>See also <a href="https://sciml.ai/community/">SciML Community page</a></li></ul></li></ul><h2 id="Reproducibility"><a class="docs-heading-anchor" href="#Reproducibility">Reproducibility</a><a id="Reproducibility-1"></a><a class="docs-heading-anchor-permalink" href="#Reproducibility" title="Permalink"></a></h2><details><summary>The documentation of this SciML package was built using these direct dependencies,</summary><pre class="documenter-example-output"><code class="nohighlight hljs ansi">Status `~/work/DiffEqBayes.jl/DiffEqBayes.jl/docs/Project.toml`
   [6e4b80f9] BenchmarkTools v1.4.0
   [593b3428] CmdStan v6.6.0
-  [ebbdde9d] DiffEqBayes v3.6.0 `~/work/DiffEqBayes.jl/DiffEqBayes.jl`
+  [ebbdde9d] DiffEqBayes v3.6.1 `~/work/DiffEqBayes.jl/DiffEqBayes.jl`
   [31c24e10] Distributions v0.25.104
 ⌅ [e30172f5] Documenter v0.27.25
   [bbc10e6e] DynamicHMC v3.4.7
@@ -91,13 +91,13 @@
   [a8cc5b0e] Crayons v4.1.1
   [9a962f9c] DataAPI v1.15.0
   [a93c6f00] DataFrames v1.6.1
-  [864edb3b] DataStructures v0.18.15
+  [864edb3b] DataStructures v0.18.16
   [e2d170a0] DataValueInterfaces v1.0.0
   [244e2a9f] DefineSingletons v0.1.2
   [8bb1440f] DelimitedFiles v1.9.1
   [b429d917] DensityInterface v0.4.0
 ⌅ [2b5f629d] DiffEqBase v6.108.0
-  [ebbdde9d] DiffEqBayes v3.6.0 `~/work/DiffEqBayes.jl/DiffEqBayes.jl`
+  [ebbdde9d] DiffEqBayes v3.6.1 `~/work/DiffEqBayes.jl/DiffEqBayes.jl`
 ⌃ [459566f4] DiffEqCallbacks v2.35.0
   [163ba53b] DiffResults v1.1.0
   [b552c78f] DiffRules v1.15.1
@@ -308,7 +308,7 @@
   [d5829a12] TriangularSolve v0.1.20
   [410a4b4d] Tricks v0.1.8
   [9d95972d] TupleTools v1.4.3
-⌅ [fce5fe82] Turing v0.28.3
+⌃ [fce5fe82] Turing v0.28.3
   [5c2747f8] URIs v1.5.1
   [3a884ed6] UnPack v1.0.2
   [1cfade01] UnicodeFun v0.4.1
@@ -458,5 +458,5 @@
   [8e850ede] nghttp2_jll v1.52.0+1
   [3f19e933] p7zip_jll v17.4.0+2
 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`</code></pre></details>You can also download the 
-<a href="https://github.com/SciML/DiffEqBayes.jl/tree/gh-pages/v3.6.0/assets/Manifest.toml">manifest</a> file and the
-<a href="https://github.com/SciML/DiffEqBayes.jl/tree/gh-pages/v3.6.0/assets/Project.toml">project</a> file.</article><nav class="docs-footer"><a class="docs-footer-nextpage" href="methods/">Methods »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Friday 5 January 2024 06:07">Friday 5 January 2024</span>. Using Julia version 1.10.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+<a href="https://github.com/SciML/DiffEqBayes.jl/tree/gh-pages/v3.6.1/assets/Manifest.toml">manifest</a> file and the
+<a href="https://github.com/SciML/DiffEqBayes.jl/tree/gh-pages/v3.6.1/assets/Project.toml">project</a> file.</article><nav class="docs-footer"><a class="docs-footer-nextpage" href="methods/">Methods »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Friday 5 January 2024 07:11">Friday 5 January 2024</span>. Using Julia version 1.10.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

dev/methods/index.html

@@ -10,4 +10,4 @@
 end</code></pre><p><code>turing_inference</code> uses <a href="https://github.com/TuringLang/Turing.jl">Turing.jl</a> to perform its parameter inference. <code>prob</code> can be any <code>DEProblem</code> with a corresponding <code>alg</code> choice. <code>t</code> is the array of time points and <code>data</code> is the set of observations for the differential equation system at time point <code>t[i]</code> (or higher dimensional). <code>priors</code> is an array of prior distributions for each parameter, specified via a <a href="https://juliastats.github.io/Distributions.jl/dev/">Distributions.jl</a> type. <code>num_samples</code> is the number of samples per MCMC chain. Sampling from multiple chains is possible, see <a href="https://turinglang.org/v0.26/docs/using-turing/guide#sampling-multiple-chains"><code>Turing.jl</code> documentation</a>, serially or parallelly using <code>parallel_type</code> and <code>n_chains</code>. The extra <code>kwargs</code> are given to the internal differential equation solver.</p><h3 id="dynamichmc_inference"><a class="docs-heading-anchor" href="#dynamichmc_inference">dynamichmc_inference</a><a id="dynamichmc_inference-1"></a><a class="docs-heading-anchor-permalink" href="#dynamichmc_inference" title="Permalink"></a></h3><pre><code class="language-julia hljs">dynamichmc_inference(prob::DEProblem, alg, t, data, priors, transformations;
                      σ = 0.01, ϵ = 0.001, initial = Float64[])</code></pre><p><code>dynamichmc_inference</code> uses <a href="https://github.com/tpapp/DynamicHMC.jl">DynamicHMC.jl</a> to perform the Bayesian parameter estimation. <code>prob</code> can be any <code>DEProblem</code>, <code>data</code> is the set of observations for our model which is to be used in the Bayesian Inference process. <code>priors</code> represent the choice of prior distributions for the parameters to be determined, passed as an array of <a href="https://juliastats.github.io/Distributions.jl/dev/">Distributions.jl</a> distributions. <code>t</code> is the array of time points. <code>transformations</code> is an array of <a href="https://github.com/tpapp/ContinuousTransformations.jl">Transformations</a> imposed for constraining the parameter values to specific domains. <code>initial</code> values for the parameters can be passed, if not passed the means of the <code>priors</code> are used. <code>ϵ</code> can be used as a kwarg to pass the initial step size for the NUTS algorithm.</p><h3 id="abc_inference"><a class="docs-heading-anchor" href="#abc_inference">abc_inference</a><a id="abc_inference-1"></a><a class="docs-heading-anchor-permalink" href="#abc_inference" title="Permalink"></a></h3><pre><code class="language-julia hljs">abc_inference(prob::DEProblem, alg, t, data, priors; ϵ = 0.001,
               distancefunction = euclidean, ABCalgorithm = ABCSMC, progress = false,
-              num_samples = 500, maxiterations = 10^5, kwargs...)</code></pre><p><code>abc_inference</code> uses <a href="https://github.com/marcjwilliams1/ApproxBayes.jl">ApproxBayes.jl</a>, which uses Approximate Bayesian Computation (ABC) to perform its parameter inference. <code>prob</code> can be any <code>DEProblem</code> with a corresponding <code>alg</code> choice. <code>t</code> is the array of time points and <code>data[:,i]</code> is the set of observations for the differential equation system at time point <code>t[i]</code> (or higher dimensional). <code>priors</code> is an array of prior distributions for each parameter, specified via a <a href="https://juliastats.github.io/Distributions.jl/dev/">Distributions.jl</a> type. <code>num_samples</code> is the number of posterior samples. <code>ϵ</code> is the target distance between the data and simulated data. <code>distancefunction</code> is a distance metric specified from the <a href="https://github.com/JuliaStats/Distances.jl">Distances.jl</a> package, the default is <code>euclidean</code>. <code>ABCalgorithm</code> is the ABC algorithm to use, options are <code>ABCSMC</code> or <code>ABCRejection</code> from <a href="https://github.com/marcjwilliams1/ApproxBayes.jl">ApproxBayes.jl</a>, the default is the former which is more efficient. <code>maxiterations</code> is the maximum number of iterations before the algorithm terminates. The extra <code>kwargs</code> are given to the internal differential equation solver.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../">« DiffEqBayes.jl: Bayesian Parameter Estimation for Differential Equations</a><a class="docs-footer-nextpage" href="../examples/">Bayesian Inference of ODE »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Friday 5 January 2024 06:07">Friday 5 January 2024</span>. Using Julia version 1.10.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+              num_samples = 500, maxiterations = 10^5, kwargs...)</code></pre><p><code>abc_inference</code> uses <a href="https://github.com/marcjwilliams1/ApproxBayes.jl">ApproxBayes.jl</a>, which uses Approximate Bayesian Computation (ABC) to perform its parameter inference. <code>prob</code> can be any <code>DEProblem</code> with a corresponding <code>alg</code> choice. <code>t</code> is the array of time points and <code>data[:,i]</code> is the set of observations for the differential equation system at time point <code>t[i]</code> (or higher dimensional). <code>priors</code> is an array of prior distributions for each parameter, specified via a <a href="https://juliastats.github.io/Distributions.jl/dev/">Distributions.jl</a> type. <code>num_samples</code> is the number of posterior samples. <code>ϵ</code> is the target distance between the data and simulated data. <code>distancefunction</code> is a distance metric specified from the <a href="https://github.com/JuliaStats/Distances.jl">Distances.jl</a> package, the default is <code>euclidean</code>. <code>ABCalgorithm</code> is the ABC algorithm to use, options are <code>ABCSMC</code> or <code>ABCRejection</code> from <a href="https://github.com/marcjwilliams1/ApproxBayes.jl">ApproxBayes.jl</a>, the default is the former which is more efficient. <code>maxiterations</code> is the maximum number of iterations before the algorithm terminates. The extra <code>kwargs</code> are given to the internal differential equation solver.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../">« DiffEqBayes.jl: Bayesian Parameter Estimation for Differential Equations</a><a class="docs-footer-nextpage" href="../examples/">Bayesian Inference of ODE »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Friday 5 January 2024 07:11">Friday 5 January 2024</span>. Using Julia version 1.10.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>