c(person("Aki", "Vehtari", email = "Aki.Vehtari@aalto.fi", role = c("aut")),
person("Andrew", "Gelman", email = "gelman@stat.columbia.edu", role = c("aut")),
person("Jonah", "Gabry", email = "jsg2201@columbia.edu", role = c("cre", "aut")),
person("Juho", "Piironen", role = c("ctb")),
person("Ben", "Goodrich", role = c("ctb")))
Aki Vehtari [aut],
Andrew Gelman [aut],
Jonah Gabry [cre, aut],
Juho Piironen [ctb],
Ben Goodrich [ctb]

Efficient approximate leave-one-out cross-validation (LOO)
using 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. We also compute the
widely applicable information criterion (WAIC).

Efficient approximate leave-one-out cross-validation (LOO)
using 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. We also compute the
widely applicable information criterion (WAIC).

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

knitr, rmarkdown, rstan, rstanarm, testthat

c(person("Aki", "Vehtari", email = "Aki.Vehtari@aalto.fi", role = c("aut")),
person("Andrew", "Gelman", role = c("aut")),
person("Jonah", "Gabry", email = "jsg2201@columbia.edu", role = c("cre", "aut")),
person("Yuling", "Yao", role = c("aut")),
person("Juho", "Piironen", role = c("ctb")),
person("Ben", "Goodrich", role = c("ctb")))
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.
graphics, matrixStats (>= 0.52), parallel, stats
bayesplot (>= 1.5.0), knitr, rmarkdown, rstan, rstanarm,
rstantools, testthat
Aki Vehtari [aut],
Andrew Gelman [aut],
Jonah Gabry [cre, aut],
Yuling Yao [aut],
Juho Piironen [ctb],
Ben Goodrich [ctb]