Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Models
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Documentation for package ‘loo’ version 2.6.0
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A C E G I K L M N O P R S T U V W
| loo-package | Efficient LOO-CV and WAIC for Bayesian models |
-- A --
| 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 |
-- C --
| compare | Model comparison (deprecated, old version) |
| crps | Continuously ranked probability score |
| crps.matrix | Continuously ranked probability score |
| crps.numeric | Continuously ranked probability score |
-- E --
| elpd | Generic (expected) log-predictive density |
| elpd.array | Generic (expected) log-predictive density |
| elpd.matrix | Generic (expected) log-predictive density |
| 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 |
-- G --
| gpdfit | Estimate parameters of the Generalized Pareto distribution |
-- I --
| 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) |
-- K --
| 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 |
-- L --
| 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_crps | Continuously ranked probability score |
| loo_crps.matrix | Continuously ranked probability score |
| 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_moment_match | Moment matching for efficient approximate leave-one-out cross-validation (LOO) |
| loo_moment_match.default | 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) |
| loo_predictive_metric | Estimate leave-one-out predictive performance.. |
| loo_predictive_metric.matrix | Estimate leave-one-out predictive performance.. |
| loo_scrps | Continuously ranked probability score |
| loo_scrps.matrix | Continuously ranked probability score |
| 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 |
-- M --
| mcse_loo | Diagnostics for Pareto smoothed importance sampling (PSIS) |
| milk | Datasets for loo examples and vignettes |
-- N --
| nobs.psis_loo_ss | The number of observations in a 'psis_loo_ss' object. |
-- O --
| obs_idx | Get observation indices used in subsampling |
-- P --
| pareto-k-diagnostic | Diagnostics for Pareto smoothed importance sampling (PSIS) |
| pareto_k_ids | Diagnostics for Pareto smoothed importance sampling (PSIS) |
| pareto_k_influence_values | 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) |
-- R --
| 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 |
-- S --
| scrps | Continuously ranked probability score |
| scrps.matrix | Continuously ranked probability score |
| scrps.numeric | Continuously ranked probability score |
| 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 |
-- T --
| tis | Truncated importance sampling (TIS) |
| tis.array | Truncated importance sampling (TIS) |
| tis.default | Truncated importance sampling (TIS) |
| tis.matrix | Truncated importance sampling (TIS) |
-- U --
| update.psis_loo_ss | Update 'psis_loo_ss' objects |
-- V --
| voice | Datasets for loo examples and vignettes |
| voice_loo | Datasets for loo examples and vignettes |
-- W --
| 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 | Extract importance sampling weights |