Description: Efficient approximate leave-one-out cross-validation (LOO)
regularizing importance weights. As a byproduct of the calculations, we also
the comparison of predictive errors between models. We also compute the

obtain approximate standard errors for estimated predictive errors and for

Imports: graphics, matrixStats (>= 0.50.0), parallel, stats

Suggests: knitr, rmarkdown, rstan, rstanarm, testthat

person("Andrew", "Gelman", role = c("aut")),
person("Yuling", "Yao", role = c("aut")),
for Bayesian models fit using Markov chain Monte Carlo. The approximation
uses Pareto smoothed importance sampling (PSIS), a new procedure 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.
Imports: graphics, matrixStats (>= 0.52), parallel, stats
Suggests: bayesplot (>= 1.5.0), knitr, rmarkdown, rstan, rstanarm,

person("Paul-Christian", "Bürkner", role = c("ctb")),
person("Mans", "Magnusson", role = c("ctb")))
Imports: graphics, matrixStats (>= 0.52), parallel, stats, checkmate
Suggests: bayesplot (>= 1.5.0), brms (>= 2.2.0), ggplot2, knitr,
rmarkdown, rstan, rstanarm (>= 2.17.4), rstantools, spdep,

person("Mans", "Magnusson", role = c("aut")),
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
Imports: checkmate, matrixStats (>= 0.52), parallel, stats
Suggests: bayesplot (>= 1.7.0), brms (>= 2.10.0), ggplot2, graphics,
knitr, rmarkdown, rstan, rstanarm (>= 2.19.0), rstantools,

person("Paul-Christian", "Bürkner", role = c("aut")),
person("Topi", "Paananen", role = c("aut")),