Package index • 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)