Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models R package loo version 2.10.1 CRAN Task View: Bayesian (https://CRAN.R-project.org/view=Bayesian) 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) <doi:10.1007/s11222-016-9696-4>. 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. Software Depends: R (>= 3.5) Imports: checkmate, matrixStats (>= 0.52), parallel, posterior (>= 1.7.0), stats Suggests: bayesplot (>= 1.7.0), brms (>= 2.10.0), ggplot2, graphics, knitr, rmarkdown, rstan, rstanarm (>= 2.19.0), rstantools, spdep, testthat (>= 3.0) Jonah Gabry <jgabry@gmail.com> Comprehensive R Archive Network (CRAN) Aki Vehtari [aut], Jonah Gabry [cre, aut], Måns Magnusson [aut], Yuling Yao [aut], Paul-Christian Bürkner [aut], Topi Paananen [aut], Andrew Gelman [aut], Ben Goodrich [ctb], Juho Piironen [ctb], Bruno Nicenboim [ctb], Leevi Lindgren [ctb], Visruth Srimath Kandali [ctb], Florence Bockting [ctb] GPL (>= 3) 2026-07-24 application/tgz https://CRAN.R-project.org/package=loo doi:10.32614/CRAN.package.loo