Aggregate documentation

ArviZ/dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-12-21T23:45:37","documenter_version":"1.2.1"}}
+{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-12-22T04:18:26","documenter_version":"1.2.1"}}

ArviZ/dev/api/data/index.html

@@ -10,12 +10,12 @@
     predictions,
     log_likelihood;
     kwargs...
-) -&gt; InferenceData</code></pre><p>Convert data in an <code>MCMCChains.Chains</code> format into an <a href="../inference_data/#InferenceObjects.InferenceData"><code>InferenceData</code></a>.</p><p>Any keyword argument below without an an explicitly annotated type above is allowed, so long as it can be passed to <a href="../inference_data/#InferenceObjects.convert_to_inference_data"><code>convert_to_inference_data</code></a>.</p><p><strong>Arguments</strong></p><ul><li><code>posterior::MCMCChains.Chains</code>: Draws from the posterior</li></ul><p><strong>Keywords</strong></p><ul><li><code>posterior_predictive::Any=nothing</code>: Draws from the posterior predictive distribution or   name(s) of predictive variables in <code>posterior</code></li><li><code>predictions</code>: Out-of-sample predictions for the posterior.</li><li><code>prior</code>: Draws from the prior</li><li><code>prior_predictive</code>: Draws from the prior predictive distribution or name(s) of predictive   variables in <code>prior</code></li><li><code>observed_data</code>: Observed data on which the <code>posterior</code> is conditional. It should only   contain data which is modeled as a random variable. Keys are parameter names and values.</li><li><code>constant_data</code>: Model constants, data included in the model that are not modeled as   random variables. Keys are parameter names.</li><li><code>predictions_constant_data</code>: Constants relevant to the model predictions (i.e. new <code>x</code>   values in a linear regression).</li><li><code>log_likelihood</code>: Pointwise log-likelihood for the data. It is recommended to use this   argument as a named tuple whose keys are observed variable names and whose values are log   likelihood arrays. Alternatively, provide the name of variable in <code>posterior</code> containing   log likelihoods.</li><li><code>library=MCMCChains</code>: Name of library that generated the chains</li><li><code>coords</code>: Map from named dimension to named indices</li><li><code>dims</code>: Map from variable name to names of its dimensions</li><li><code>eltypes</code>: Map from variable names to eltypes. This is primarily used to assign discrete   eltypes to discrete variables that were stored in <code>Chains</code> as floats.</li></ul><p><strong>Returns</strong></p><ul><li><code>InferenceData</code>: The data with groups corresponding to the provided data</li></ul></div><a class="docs-sourcelink" href="https://github.com/arviz-devs/ArviZ.jl/blob/757409bfccc1ac2b57315af0ee33f278da9e09eb/src/conversions.jl#L1-L48" target="_blank">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" href="#ArviZ.from_samplechains" id="ArviZ.from_samplechains"><code>ArviZ.from_samplechains</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">from_samplechains(
+) -&gt; InferenceData</code></pre><p>Convert data in an <code>MCMCChains.Chains</code> format into an <a href="../inference_data/#InferenceObjects.InferenceData"><code>InferenceData</code></a>.</p><p>Any keyword argument below without an an explicitly annotated type above is allowed, so long as it can be passed to <a href="../inference_data/#InferenceObjects.convert_to_inference_data"><code>convert_to_inference_data</code></a>.</p><p><strong>Arguments</strong></p><ul><li><code>posterior::MCMCChains.Chains</code>: Draws from the posterior</li></ul><p><strong>Keywords</strong></p><ul><li><code>posterior_predictive::Any=nothing</code>: Draws from the posterior predictive distribution or   name(s) of predictive variables in <code>posterior</code></li><li><code>predictions</code>: Out-of-sample predictions for the posterior.</li><li><code>prior</code>: Draws from the prior</li><li><code>prior_predictive</code>: Draws from the prior predictive distribution or name(s) of predictive   variables in <code>prior</code></li><li><code>observed_data</code>: Observed data on which the <code>posterior</code> is conditional. It should only   contain data which is modeled as a random variable. Keys are parameter names and values.</li><li><code>constant_data</code>: Model constants, data included in the model that are not modeled as   random variables. Keys are parameter names.</li><li><code>predictions_constant_data</code>: Constants relevant to the model predictions (i.e. new <code>x</code>   values in a linear regression).</li><li><code>log_likelihood</code>: Pointwise log-likelihood for the data. It is recommended to use this   argument as a named tuple whose keys are observed variable names and whose values are log   likelihood arrays. Alternatively, provide the name of variable in <code>posterior</code> containing   log likelihoods.</li><li><code>library=MCMCChains</code>: Name of library that generated the chains</li><li><code>coords</code>: Map from named dimension to named indices</li><li><code>dims</code>: Map from variable name to names of its dimensions</li><li><code>eltypes</code>: Map from variable names to eltypes. This is primarily used to assign discrete   eltypes to discrete variables that were stored in <code>Chains</code> as floats.</li></ul><p><strong>Returns</strong></p><ul><li><code>InferenceData</code>: The data with groups corresponding to the provided data</li></ul></div><a class="docs-sourcelink" href="https://github.com/arviz-devs/ArviZ.jl/blob/7d66a3cffc7f03814c9d8ba5fe00e9f676008d65/src/conversions.jl#L1-L48" target="_blank">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" href="#ArviZ.from_samplechains" id="ArviZ.from_samplechains"><code>ArviZ.from_samplechains</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">from_samplechains(
     posterior=nothing;
     prior=nothing,
     library=SampleChains,
     kwargs...,
-) -&gt; InferenceData</code></pre><p>Convert SampleChains samples to an <code>InferenceData</code>.</p><p>Either <code>posterior</code> or <code>prior</code> may be a <code>SampleChains.AbstractChain</code> or <code>SampleChains.MultiChain</code> object.</p><p>For descriptions of remaining <code>kwargs</code>, see <a href="../inference_data/#InferenceObjects.from_namedtuple"><code>from_namedtuple</code></a>.</p></div><a class="docs-sourcelink" href="https://github.com/arviz-devs/ArviZ.jl/blob/757409bfccc1ac2b57315af0ee33f278da9e09eb/src/conversions.jl#L51-L65" target="_blank">source</a></section></article><h2 id="IO-/-Conversion"><a class="docs-heading-anchor" href="#IO-/-Conversion">IO / Conversion</a><a id="IO-/-Conversion-1"></a><a class="docs-heading-anchor-permalink" href="#IO-/-Conversion" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" href="#InferenceObjects.from_netcdf" id="InferenceObjects.from_netcdf"><code>InferenceObjects.from_netcdf</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">from_netcdf(path::AbstractString; kwargs...) -&gt; InferenceData</code></pre><p>Load an <a href="../inference_data/#InferenceObjects.InferenceData"><code>InferenceData</code></a> from an unopened NetCDF file.</p><p>Remaining <code>kwargs</code> are passed to <a href="https://alexander-barth.github.io/NCDatasets.jl/stable/dataset/#NCDatasets.NCDataset"><code>NCDatasets.NCDataset</code></a>. This method loads data eagerly. To instead load data lazily, pass an opened <code>NCDataset</code> to <code>from_netcdf</code>.</p><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>This method requires that NCDatasets is loaded before it can be used.</p></div></div><p><strong>Examples</strong></p><pre><code class="language-julia hljs">julia&gt; using InferenceObjects, NCDatasets
+) -&gt; InferenceData</code></pre><p>Convert SampleChains samples to an <code>InferenceData</code>.</p><p>Either <code>posterior</code> or <code>prior</code> may be a <code>SampleChains.AbstractChain</code> or <code>SampleChains.MultiChain</code> object.</p><p>For descriptions of remaining <code>kwargs</code>, see <a href="../inference_data/#InferenceObjects.from_namedtuple"><code>from_namedtuple</code></a>.</p></div><a class="docs-sourcelink" href="https://github.com/arviz-devs/ArviZ.jl/blob/7d66a3cffc7f03814c9d8ba5fe00e9f676008d65/src/conversions.jl#L51-L65" target="_blank">source</a></section></article><h2 id="IO-/-Conversion"><a class="docs-heading-anchor" href="#IO-/-Conversion">IO / Conversion</a><a id="IO-/-Conversion-1"></a><a class="docs-heading-anchor-permalink" href="#IO-/-Conversion" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" href="#InferenceObjects.from_netcdf" id="InferenceObjects.from_netcdf"><code>InferenceObjects.from_netcdf</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">from_netcdf(path::AbstractString; kwargs...) -&gt; InferenceData</code></pre><p>Load an <a href="../inference_data/#InferenceObjects.InferenceData"><code>InferenceData</code></a> from an unopened NetCDF file.</p><p>Remaining <code>kwargs</code> are passed to <a href="https://alexander-barth.github.io/NCDatasets.jl/stable/dataset/#NCDatasets.NCDataset"><code>NCDatasets.NCDataset</code></a>. This method loads data eagerly. To instead load data lazily, pass an opened <code>NCDataset</code> to <code>from_netcdf</code>.</p><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>This method requires that NCDatasets is loaded before it can be used.</p></div></div><p><strong>Examples</strong></p><pre><code class="language-julia hljs">julia&gt; using InferenceObjects, NCDatasets
 
 julia&gt; idata = from_netcdf("centered_eight.nc")
 InferenceData with groups:
@@ -47,4 +47,4 @@
   &gt; posterior
 
 julia&gt; to_netcdf(idata, "data.nc")
-"data.nc"</code></pre></div><a class="docs-sourcelink" href="https://github.com/arviz-devs/InferenceObjects.jl/blob/v0.3.14/src/io.jl#L63-L92" target="_blank">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../diagnostics/">« Diagnostics</a><a class="docs-footer-nextpage" href="../inference_data/">InferenceData »</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></p><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="auto">Automatic (OS)</option></select></div><p></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.2.1 on <span class="colophon-date" title="Thursday 21 December 2023 23:45">Thursday 21 December 2023</span>. Using Julia version 1.9.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></HTML>
+"data.nc"</code></pre></div><a class="docs-sourcelink" href="https://github.com/arviz-devs/InferenceObjects.jl/blob/v0.3.14/src/io.jl#L63-L92" target="_blank">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../diagnostics/">« Diagnostics</a><a class="docs-footer-nextpage" href="../inference_data/">InferenceData »</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></p><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="auto">Automatic (OS)</option></select></div><p></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.2.1 on <span class="colophon-date" title="Friday 22 December 2023 04:18">Friday 22 December 2023</span>. Using Julia version 1.9.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></HTML>