Function reference • loo

Package description, glossary, and included data sets
`loo-package`	Efficient LOO-CV and WAIC for Bayesian models
`loo-glossary`	LOO package glossary
`loo-datasets` `Kline` `milk` `voice` `voice_loo`	Datasets for loo examples and vignettes
Approximate LOO-CV Approximate LOO-CV, Pareto smoothed importance sampling (PSIS), and diagnostics.
`loo()` `loo_i()` `is.loo()` `is.psis_loo()`	Efficient approximate leave-one-out cross-validation (LOO)
`loo_subsample()`	Efficient approximate leave-one-out cross-validation (LOO) using subsampling, so that less costly and more approximate computation is made for all LOO-fold, and more costly and accurate computations are made only for m<N LOO-folds.
`loo_approximate_posterior()`	Efficient approximate leave-one-out cross-validation (LOO) for posterior approximations
`loo_moment_match()`	Moment matching for efficient approximate leave-one-out cross-validation (LOO)
`loo_moment_match_split()`	Split moment matching for efficient approximate leave-one-out cross-validation (LOO)
`E_loo()`	Compute weighted expectations
`psis()` `is.psis()` `is.sis()` `is.tis()`	Pareto smoothed importance sampling (PSIS)
`ap_psis()`	Pareto smoothed importance sampling (PSIS) using approximate posteriors
`tis()`	Truncated importance sampling (TIS)
`sis()`	Standard importance sampling (SIS)
`importance_sampling()`	A parent class for different importance sampling methods.
`weights(<importance_sampling>)`	Extract importance sampling weights
`pareto_k_table()` `pareto_k_ids()` `pareto_k_values()` `pareto_k_influence_values()` `psis_n_eff_values()` `mcse_loo()` `plot(<psis_loo>)` `plot(<psis>)`	Diagnostics for Pareto smoothed importance sampling (PSIS)
Model comparison weighting/averaging Functions for comparing models and computing model weights via stacking of predictive distributions or pseudo-BMA weighting.
`loo_compare()` `print(<compare.loo>)` `print(<compare.loo_ss>)`	Model comparison
`loo_model_weights()` `stacking_weights()` `pseudobma_weights()`	Model averaging/weighting via stacking or pseudo-BMA weighting
Helper functions for K-fold CV
`kfold_split_random()` `kfold_split_stratified()` `kfold_split_grouped()`	Helper functions for K-fold cross-validation
`kfold()` `is.kfold()`	Generic function for K-fold cross-validation for developers
`elpd()`	Generic (expected) log-predictive density
Other functions
`loo_predictive_metric()`	Estimate leave-one-out predictive performance..
`crps()` `scrps()` `loo_crps()` `loo_scrps()`	Continuously ranked probability score
`elpd()`	Generic (expected) log-predictive density
`waic()` `is.waic()`	Widely applicable information criterion (WAIC)
`extract_log_lik()`	Extract pointwise log-likelihood from a Stan model
`pointwise()`	Convenience function for extracting pointwise estimates
`relative_eff()`	Convenience function for computing relative efficiencies
`gpdfit()`	Estimate parameters of the Generalized Pareto distribution
`example_loglik_array()` `example_loglik_matrix()`	Objects to use in examples and tests
`print(<loo>)` `print(<waic>)` `print(<psis_loo>)` `print(<importance_sampling_loo>)` `print(<psis_loo_ap>)` `print(<psis>)` `print(<importance_sampling>)`	Print methods
`nobs(<psis_loo_ss>)`	The number of observations in a `psis_loo_ss` object.
`obs_idx()`	Get observation indices used in subsampling
`update(<psis_loo_ss>)`	Update `psis_loo_ss` objects
Deprecated functions
`compare()`	Model comparison (deprecated, old version)
`psislw()`	Pareto smoothed importance sampling (deprecated, old version)

Reference

Package description, glossary, and included data sets

Approximate LOO-CV

Model comparison weighting/averaging

Helper functions for K-fold CV

Other functions

Deprecated functions