loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

We efficiently approximate leave-one-out cross-validation (LOO) 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 the comparison of predictive errors between two models. We also compute the widely applicable information criterion (WAIC).

Version: 0.1.3
Depends: R (≥ 3.1.2)
Imports: graphics, matrixStats (≥ 0.14.1), parallel, stats
Suggests: knitr, testthat
Published: 2015-09-18
Author: Aki Vehtari [aut], Andrew Gelman [aut], Jonah Gabry [cre, aut], Juho Piironen [ctb], Ben Goodrich [ctb]
Maintainer: Jonah Gabry <jsg2201 at columbia.edu>
BugReports: https://github.com/jgabry/loo/issues
License: GPL (≥ 3)
URL: https://github.com/jgabry/loo
NeedsCompilation: no
Citation: loo citation info
CRAN checks: loo results


Reference manual: loo.pdf
Vignettes: Example
Package source: loo_0.1.3.tar.gz
Windows binaries: r-devel: loo_0.1.3.zip, r-release: loo_0.1.3.zip, r-oldrel: loo_0.1.3.zip
OS X Snow Leopard binaries: r-release: loo_0.1.2.tgz, r-oldrel: not available
OS X Mavericks binaries: r-release: loo_0.1.3.tgz
Old sources: loo archive

Reverse dependencies:

Reverse imports: blavaan, brms
Reverse suggests: rstan