(api_reference)= # API reference :::{note} Similarly to xarray, ArviZ aims for idempotent functions. However, there are two important caveats. First and foremost, several functions require combining data from multiple groups or variables, in which case the output won't keep the type of the input data. Moreover, ArviZ works on data following the InferenceData schema, there are functions that can accept PPL outputs directly, but when this happens, the first step is converting those outputs to InferenceData. Consequently, the output won't be of the same type. ::: As indicated in {ref}`installation`, the recommended way to install `arviz-stats` is with `xarray` and `arviz-base` as optional dependencies. This activates most of the features of the libraries and the bulk of this reference guide documents them. If you only installed the minimal version of `arviz-stats` you should jump to {ref}`array_stats_api` and read forward. ## Top level functions ### Diagnostics ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.bfmi arviz_stats.diagnose arviz_stats.ess arviz_stats.loo_pit arviz_stats.mcse arviz_stats.psense arviz_stats.psense_summary arviz_stats.rhat arviz_stats.rhat_nested ``` ### Statistical summaries ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.bayesian_r2 arviz_stats.bayes_factor arviz_stats.ci_in_rope arviz_stats.ecdf arviz_stats.eti arviz_stats.iqr arviz_stats.hdi arviz_stats.histogram arviz_stats.kde arviz_stats.kl_divergence arviz_stats.loo_expectations arviz_stats.loo_influence arviz_stats.loo_metrics arviz_stats.loo_r2 arviz_stats.loo_score arviz_stats.mad arviz_stats.mean arviz_stats.median arviz_stats.metrics arviz_stats.mode arviz_stats.qds arviz_stats.residual_r2 arviz_stats.std arviz_stats.summary arviz_stats.var arviz_stats.wasserstein ``` ### Model comparison ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.compare arviz_stats.loo arviz_stats.loo_i arviz_stats.loo_approximate_posterior arviz_stats.loo_kfold arviz_stats.loo_moment_match arviz_stats.loo_subsample arviz_stats.reloo arviz_stats.update_subsample ``` ### Other ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.SamplingWrapper arviz_stats.thin ``` ## Accessors In addition, many functions are also available via accessors: ### Dataset accessors ```{eval-rst} .. autosummary:: :toctree: generated/ :template: autosummary/accessor_attribute.rst xarray.Dataset.azstats.ds .. autosummary:: :toctree: generated/ :template: autosummary/accessor_method.rst xarray.Dataset.azstats.filter_vars xarray.Dataset.azstats.eti xarray.Dataset.azstats.hdi xarray.Dataset.azstats.compute_ranks xarray.Dataset.azstats.ess xarray.Dataset.azstats.rhat xarray.Dataset.azstats.rhat_nested xarray.Dataset.azstats.mcse xarray.Dataset.azstats.thin xarray.Dataset.azstats.kde xarray.Dataset.azstats.histogram xarray.Dataset.azstats.ecdf xarray.Dataset.azstats.autocorr ``` ## DataArray facing functions ### Base submodule ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.base.dataarray_stats.eti arviz_stats.base.dataarray_stats.hdi arviz_stats.base.dataarray_stats.ess arviz_stats.base.dataarray_stats.rhat arviz_stats.base.dataarray_stats.rhat_nested arviz_stats.base.dataarray_stats.mcse arviz_stats.base.dataarray_stats.histogram arviz_stats.base.dataarray_stats.kde arviz_stats.base.dataarray_stats.autocorr ``` ### Numba submodule The numba accelerated computations are available as the same methods but of the `arviz_stats.numba.dataarray_stats` class. Both their API and implementation is the same as for the base module, the only difference being that one calls `arviz_stats.base.array_stats` for array facing functions whereas the other one calls `arviz_stats.numba.array_stats`. Implementation differences are thus documented below, at the array facing classes. (array_stats_api)= ## Array facing functions All functions and methods described after this point work when installing `arviz-stats` without optional dependencies. :::{warning} Keep in mind this is not the recommended install and the main target of such functions are developers of other libraries who want to use ArviZ but keep the dependency list small. The documentation is more bare bones than other functions and will often refer you to other pages for the full argument or algorithm descriptions. ::: ### Base submodule #### Sampling diagnostics ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.base.array_stats.ess arviz_stats.base.array_stats.pareto_min_ss arviz_stats.base.array_stats.rhat arviz_stats.base.array_stats.rhat_nested arviz_stats.base.array_stats.mcse ``` #### Statistical summaries ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.base.array_stats.eti arviz_stats.base.array_stats.hdi arviz_stats.base.array_stats.histogram arviz_stats.base.array_stats.kde arviz_stats.base.array_stats.quantile ``` #### Model comparison ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.base.array_stats.loo arviz_stats.base.array_stats.loo_approximate_posterior arviz_stats.base.array_stats.loo_expectation arviz_stats.base.array_stats.loo_mixture arviz_stats.base.array_stats.loo_pit arviz_stats.base.array_stats.loo_quantile arviz_stats.base.array_stats.loo_r2 arviz_stats.base.array_stats.loo_score arviz_stats.base.array_stats.loo_summary ``` #### Other ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.base.array_stats.autocorr arviz_stats.base.array_stats.autocov arviz_stats.base.array_stats.compute_ranks arviz_stats.base.array_stats.get_bins arviz_stats.base.array_stats.psislw ``` ### Numba submodule Some functions are accelerated internally without changes to the public API, others are purely inherited from the base backend, and a last group is partially or completely reimplemented. This last group is documented here: ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_stats.numba.array_stats.quantile arviz_stats.numba.array_stats.histogram arviz_stats.numba.array_stats.kde ```