Efficient approximate leave-one-out cross-validation (LOO)
for Bayesian models fit using Markov chain Monte Carlo, as
described in Vehtari, Gelman, and Gabry (2017)
<doi:10.1007/s11222-016-9696-4>.
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.
| Version: |
2.8.0 |
| Depends: |
R (≥ 3.1.2) |
| Imports: |
checkmate, matrixStats (≥ 0.52), parallel, posterior (≥
1.5.0), stats |
| Suggests: |
bayesplot (≥ 1.7.0), brms (≥ 2.10.0), ggplot2, graphics, knitr, rmarkdown, rstan, rstanarm (≥ 2.19.0), rstantools, spdep, testthat (≥ 2.1.0) |
| Published: |
2024-07-03 |
| DOI: |
10.32614/CRAN.package.loo |
| Author: |
Aki Vehtari [aut],
Jonah Gabry [cre, aut],
Måns Magnusson [aut],
Yuling Yao [aut],
Paul-Christian Bürkner [aut],
Topi Paananen [aut],
Andrew Gelman [aut],
Ben Goodrich [ctb],
Juho Piironen [ctb],
Bruno Nicenboim [ctb],
Leevi Lindgren [ctb] |
| Maintainer: |
Jonah Gabry <jsg2201 at columbia.edu> |
| BugReports: |
https://github.com/stan-dev/loo/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://mc-stan.org/loo/, https://discourse.mc-stan.org |
| NeedsCompilation: |
no |
| SystemRequirements: |
pandoc (>= 1.12.3), pandoc-citeproc |
| Citation: |
loo citation info |
| Materials: |
NEWS |
| In views: |
Bayesian |
| CRAN checks: |
loo results |