Package

r-loo

Description

Efficient Leave-One-Out Cross-Validation and WAIC for BayesianModels.
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) <doi:10.1007/s11222-016-9696-4>. 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.

Note