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

Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo. 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.

Package details

AuthorAki Vehtari [aut], Andrew Gelman [aut], Jonah Gabry [cre, aut], Yuling Yao [aut], Paul-Christian Bürkner [ctb], Ben Goodrich [ctb], Juho Piironen [ctb], Mans Magnusson [ctb]
MaintainerJonah Gabry <[email protected]>
LicenseGPL (>= 3)
URL https://mc-stan.org/loo https://discourse.mc-stan.org
Package repositoryView on CRAN
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loo documentation built on May 2, 2019, 8:35 a.m.