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 | 
| 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) | 
| crps | Continuously ranked probability score | 
| crps.matrix | Continuously ranked probability score | 
| crps.numeric | Continuously ranked probability score | 
| 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 | 
| gpdfit | Estimate parameters of the Generalized Pareto distribution | 
| importance_sampling | A parent class for different importance sampling methods. | 
| importance_sampling.array | A parent class for different importance sampling methods. | 
| importance_sampling.default | A parent class for different importance sampling methods. | 
| importance_sampling.matrix | A parent class for different importance sampling methods. | 
| 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_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, 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_subsample.function | 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. | 
| 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_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) | 
| pointwise | Convenience function for extracting pointwise estimates | 
| pointwise.loo | Convenience function for extracting pointwise estimates | 
| 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 | 
| 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 | 
| 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 | 
| voice | Datasets for loo examples and vignettes | 
| voice_loo | Datasets for loo examples and vignettes | 
| 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 |