Package: loo
Type: Package
Title: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
        Models
Version: 0.1.2
Date: 2015-07-16
Authors@R: 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")))
Maintainer: Jonah Gabry <jsg2201@columbia.edu>
URL: https://github.com/jgabry/loo
BugReports: https://github.com/jgabry/loo/issues
Description: We efficiently approximate leave-one-out cross-validation (LOO)
  using Pareto smoothed importance sampling (PSIS), a new procedure for
  regularizing importance weights. As a byproduct of our calculations, we also
  obtain approximate standard errors for estimated predictive errors, and for
  the comparison of predictive errors between two models. We also compute the
  widely applicable information criterion (WAIC).
License: GPL (>= 3)
LazyData: TRUE
Depends: R (>= 3.1.2)
Imports: graphics, matrixStats (>= 0.14.1), parallel, stats
Suggests: knitr, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2015-07-16 16:14:33 UTC; jgabry
Author: Aki Vehtari [aut],
  Andrew Gelman [aut],
  Jonah Gabry [cre, aut],
  Juho Piironen [ctb]
Repository: CRAN
Date/Publication: 2015-07-17 00:18:06
