loo is an R package that allows users to compute efficient approximate leave-one-out cross-validation for fitted Bayesian models, as well as model weights that can be used to average predictive distributions. The loo package package implements the fast and stable computations for approximate LOO-CV and WAIC from
and computes model weights as described in
From existing posterior simulation draws, we compute approximate LOO-CV using Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors and for comparing predictive errors between two models. We recommend PSIS-LOO-CV instead of WAIC, because PSIS provides useful diagnostics and effective sample size and Monte Carlo standard error estimates.
# install.packages("remotes") remotes::install_github("stan-dev/loo")
We do not recommend setting
build_vignettes=TRUE when installing from GitHub because some of the vignettes take a long time to build and are always available online at mc-stan.org/loo/articles/.
Corresponding Python and Matlab/Octave code can be found at the avehtari/PSIS repository.