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).
| Version: |
1.1.0 |
| Depends: |
R (≥ 3.1.2) |
| Imports: |
graphics, matrixStats (≥ 0.50.0), parallel, stats |
| Suggests: |
knitr, rmarkdown, rstan, rstanarm, testthat |
| Published: |
2017-03-27 |
| Author: |
Aki Vehtari [aut],
Andrew Gelman [aut],
Jonah Gabry [cre, aut],
Juho Piironen [ctb],
Ben Goodrich [ctb] |
| Maintainer: |
Jonah Gabry <jsg2201 at columbia.edu> |
| BugReports: |
https://github.com/stan-dev/loo/issues |
| License: |
GPL (≥ 3) |
| URL: |
http://mc-stan.org/,
https://groups.google.com/forum/#!forum/stan-users |
| NeedsCompilation: |
no |
| Citation: |
loo citation info |
| Materials: |
NEWS |
| CRAN checks: |
loo results |