loo (0.1.4)

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

https://github.com/jgabry/loo
http://cran.r-project.org/web/packages/loo

Efficient approximate leave-one-out cross-validation (LOO) using 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. We also compute the widely applicable information criterion (WAIC).

Maintainer: Jonah Gabry
Author(s): Aki Vehtari [aut], Andrew Gelman [aut], Jonah Gabry [cre, aut], Juho Piironen [ctb], Ben Goodrich [ctb]

License: GPL (>= 3)

Uses: matrixStats, testthat, knitr
Reverse depends: evidence, hBayesDM
Reverse suggests: bayesplot, bayesvl, CopulaDTA, idealstan, performance, psycho, rstan, rstantools, sjstats

Released almost 4 years ago.