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loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models
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.
Getting started
Package overview
Approximate leave-future-out cross-validation for Bayesian time series models
Avoiding model refits in leave-one-out cross-validation with moment matching
Bayesian Stacking and Pseudo-BMA weights using the loo package
Holdout validation and K-fold cross-validation of Stan programs with the loo package
Leave-one-out cross-validation for non-factorized models
Mixture IS leave-one-out cross-validation for high-dimensional Bayesian models
Using Leave-one-out cross-validation for large data
Using the loo package (version >= 2.0.0)
Writing Stan programs for use with the loo package
Browse package contents
Package details |
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| Author | Aki Vehtari [aut], Jonah Gabry [cre, aut], Mans 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@columbia.edu> |
| License | GPL (>= 3) |
| Version | 2.6.0 |
| URL | https://mc-stan.org/loo/ https://discourse.mc-stan.org |
| Package repository | View on CRAN |
| Installation |
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