r-cran-loo - 2.8.0-1 main

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, it is possible
as well to 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.