loo (version 2.5.1)

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

Description

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) . 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.

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Install

install.packages('loo')

Monthly Downloads

43,442

Version

2.5.1

License

GPL (>= 3)

Maintainer

Last Published

March 24th, 2022

Functions in loo (2.5.1)