Package: loo
Type: Package
Title: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
        Models
Version: 0.1.0
Date: 2015-06-24
Author: Aki Vehtari, Andrew Gelman, Jonah Gabry
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 very good importance sampling (VGIS), 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 (>= 3.2.0)
Imports: matrixStats (>= 0.14.1), parallel
Suggests: knitr, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2015-06-26 01:00:17 UTC; jgabry
Repository: CRAN
Date/Publication: 2015-06-26 10:55:18
