Articles
Getting started
These vignettes demonstrate how to use the loo package to perform approximate leave-one-out cross-validation or exact K-fold cross-validation for Bayesian models fit using MCMC, compare models on estimated predictive performance on new data, and weight models for averaging predictive distributions.
Additional topics
These vignettes demonstrate how to use the loo package for more complicated scenarios including models with non-factorized likelihoods, forecasting models, models fit to very large datasets, and more.
- Leave-one-out cross-validation for non-factorized models
- Approximate leave-future-out cross-validation for Bayesian time series models
- Using Leave-one-out cross-validation for large data
- Avoiding model refits in leave-one-out cross-validation with moment matching
- Mixture IS leave-one-out cross-validation for high-dimensional Bayesian models
Frequently asked questions
- Cross-validation FAQ
Answers to frequently asked questions about cross-validation and the loo package (links to external site).