# Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

## Documentation for package ‘loo’ version 2.2.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

loo-package | Efficient LOO-CV and WAIC for Bayesian models
ap_psis | Pareto smoothed importance sampling (PSIS) using approximate posteriors
ap_psis.array | Pareto smoothed importance sampling (PSIS) using approximate posteriors
ap_psis.default | Pareto smoothed importance sampling (PSIS) using approximate posteriors
ap_psis.matrix | Pareto smoothed importance sampling (PSIS) using approximate posteriors
compare | Model comparison (deprecated, old version)
example_loglik_array | Objects to use in examples and tests
example_loglik_matrix | Objects to use in examples and tests
extract_log_lik | Extract pointwise log-likelihood from a Stan model
E_loo | Compute weighted expectations
E_loo.default | Compute weighted expectations
E_loo.matrix | Compute weighted expectations
gpdfit | Estimate parameters of the Generalized Pareto distribution
is.kfold | Generic function for K-fold cross-validation for developers
is.loo | Efficient approximate leave-one-out cross-validation (LOO)
is.psis | Pareto smoothed importance sampling (PSIS)
is.psis_loo | Efficient approximate leave-one-out cross-validation (LOO)
is.sis | Pareto smoothed importance sampling (PSIS)
is.tis | Pareto smoothed importance sampling (PSIS)
is.waic | Widely applicable information criterion (WAIC)
kfold | Generic function for K-fold cross-validation for developers
kfold-generic | Generic function for K-fold cross-validation for developers
kfold-helpers | Helper functions for K-fold cross-validation
kfold_split_grouped | Helper functions for K-fold cross-validation
kfold_split_random | Helper functions for K-fold cross-validation
kfold_split_stratified | Helper functions for K-fold cross-validation
Kline | Datasets for loo examples and vignettes
loo | Efficient approximate leave-one-out cross-validation (LOO)
loo-datasets | Datasets for loo examples and vignettes
loo-glossary | LOO package glossary
loo.array | Efficient approximate leave-one-out cross-validation (LOO)
loo.function | Efficient approximate leave-one-out cross-validation (LOO)
loo.matrix | Efficient approximate leave-one-out cross-validation (LOO)
loo_approximate_posterior | Efficient approximate leave-one-out cross-validation (LOO) for posterior approximations
loo_approximate_posterior.array | Efficient approximate leave-one-out cross-validation (LOO) for posterior approximations
loo_approximate_posterior.function | Efficient approximate leave-one-out cross-validation (LOO) for posterior approximations
loo_approximate_posterior.matrix | Efficient approximate leave-one-out cross-validation (LOO) for posterior approximations
loo_compare | Model comparison
loo_compare.default | Model comparison
loo_i | Efficient approximate leave-one-out cross-validation (LOO)
loo_model_weights | Model averaging/weighting via stacking or pseudo-BMA weighting
loo_model_weights.default | Model averaging/weighting via stacking or pseudo-BMA weighting
loo_subsample | Efficient approximate leave-one-out cross-validation (LOO) using subsampling
loo_subsample.function | Efficient approximate leave-one-out cross-validation (LOO) using subsampling
mcse_loo | Diagnostics for Pareto smoothed importance sampling (PSIS)
milk | Datasets for loo examples and vignettes
nobs.psis_loo_ss | The number of observations in a 'psis_loo_ss' object.
obs_idx | Get observation indices used in subsampling
pareto-k-diagnostic | Diagnostics for Pareto smoothed importance sampling (PSIS)
pareto_k_ids | Diagnostics for Pareto smoothed importance sampling (PSIS)
pareto_k_table | Diagnostics for Pareto smoothed importance sampling (PSIS)
pareto_k_values | Diagnostics for Pareto smoothed importance sampling (PSIS)
plot.loo | Diagnostics for Pareto smoothed importance sampling (PSIS)
plot.psis | Diagnostics for Pareto smoothed importance sampling (PSIS)
plot.psis_loo | Diagnostics for Pareto smoothed importance sampling (PSIS)
print.compare.loo | Model comparison
print.compare.loo_ss | Model comparison
print.importance_sampling | Print methods
print.importance_sampling_loo | Print methods
print.loo | Print methods
print.psis | Print methods
print.psis_loo | Print methods
print.psis_loo_ap | Print methods
print.waic | Print methods
pseudobma_weights | Model averaging/weighting via stacking or pseudo-BMA weighting
psis | Pareto smoothed importance sampling (PSIS)
psis.array | Pareto smoothed importance sampling (PSIS)
psis.default | Pareto smoothed importance sampling (PSIS)
psis.matrix | Pareto smoothed importance sampling (PSIS)
psislw | Pareto smoothed importance sampling (deprecated, old version)
psis_n_eff_values | Diagnostics for Pareto smoothed importance sampling (PSIS)
relative_eff | Convenience function for computing relative efficiencies
relative_eff.array | Convenience function for computing relative efficiencies
relative_eff.default | Convenience function for computing relative efficiencies
relative_eff.function | Convenience function for computing relative efficiencies
relative_eff.importance_sampling | Convenience function for computing relative efficiencies
relative_eff.matrix | Convenience function for computing relative efficiencies
sis | Standard importance sampling (SIS)
sis.array | Standard importance sampling (SIS)
sis.default | Standard importance sampling (SIS)
sis.matrix | Standard importance sampling (SIS)
stacking_weights | Model averaging/weighting via stacking or pseudo-BMA weighting
tis | Truncated importance sampling (TIS)
tis.array | Truncated importance sampling (TIS)
tis.default | Truncated importance sampling (TIS)
tis.matrix | Truncated importance sampling (TIS)
update.psis_loo_ss | Update 'psis_loo_ss' objects
waic | Widely applicable information criterion (WAIC)
waic.array | Widely applicable information criterion (WAIC)
waic.function | Widely applicable information criterion (WAIC)
waic.matrix | Widely applicable information criterion (WAIC)
weights.importance_sampling | Pareto smoothed importance sampling (PSIS)