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loo  

Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models
View on CRAN: Click here


Download and install loo package within the R console
Install from CRAN:
install.packages("loo")

Install from Github:
library("remotes")
install_github("cran/loo")

Install by package version:
library("remotes")
install_version("loo", "2.8.0")



Attach the package and use:
library("loo")
Maintained by
Jonah Gabry
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-06-26
Latest Update: 2024-02-24
Description:
Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo, as described in Vehtari, Gelman, and Gabry (2017) . The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.
How to cite:
Jonah Gabry (2015). loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models. R package version 2.8.0, https://cran.r-project.org/web/packages/loo. Accessed 03 Dec. 2024.
Previous versions and publish date:
0.1.0 (2015-06-26 10:55), 0.1.2 (2015-07-17 00:18), 0.1.3 (2015-09-18 08:58), 0.1.4 (2015-12-10 09:49), 0.1.5 (2016-02-12 07:41), 0.1.6 (2016-03-23 22:30), 1.0.0 (2016-12-16 08:33), 1.1.0 (2017-03-27 17:21), 2.0.0 (2018-04-11 14:35), 2.1.0 (2019-03-13 22:40), 2.2.0 (2019-12-19 10:40), 2.3.0 (2020-07-07 14:50), 2.3.1 (2020-07-15 00:50), 2.4.0 (2020-12-05 02:20), 2.4.1 (2020-12-09 23:50), 2.5.0 (2022-03-16 17:10), 2.5.1 (2022-03-24 11:30), 2.6.0 (2023-03-31 11:20), 2.7.0 (2024-02-25 00:20)
Other packages that cited loo R package
View loo citation profile
Other R packages that loo depends, imports, suggests or enhances
Complete documentation for loo
Functions, R codes and Examples using the loo R package
Some associated functions: E_loo . ap_psis . compare . crps . dot-compute_point_estimate . dot-ndraws . dot-thin_draws . elpd . example_loglik_array . extract_log_lik . find_model_names . gpdfit . importance_sampling.array . importance_sampling.default . importance_sampling.matrix . importance_sampling . kfold-generic . kfold-helpers . loo-datasets . loo-glossary . loo-package . loo . loo_approximate_posterior . loo_compare . loo_model_weights . loo_moment_match . loo_moment_match_split . loo_predictive_metric . loo_subsample . nlist . nobs.psis_loo_ss . obs_idx . old-extractors . parallel_psis_list . pareto-k-diagnostic . print.loo . print_dims . psis . psis_approximate_posterior . psislw . relative_eff . sis . tis . update.psis_loo_ss . waic . weights.importance_sampling . 
Some associated R codes: E_loo.R . compare.R . crps.R . datasets.R . diagnostics.R . effective_sample_sizes.R . elpd.R . example_log_lik_array.R . extract_log_lik.R . gpdfit.R . helpers.R . importance_sampling.R . kfold-generic.R . kfold-helpers.R . loo-glossary.R . loo-package.R . loo.R . loo_approximate_posterior.R . loo_compare.R . loo_compare.psis_loo_ss_list.R . loo_model_weights.R . loo_moment_matching.R . loo_predictive_metric.R . loo_subsample.R . print.R . psis.R . psis_approximate_posterior.R . psislw.R . sis.R . split_moment_matching.R . tis.R . waic.R . zzz.R .  Full loo package functions and examples
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