loo
Software:31247
swMATH19420CRANlooMaRDI QIDQ31247
Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models
Author name not available (Why is that?)
Last update: 24 February 2024
Copyright license: No records found.
Software version identifier: 2.6.0, 0.1.0, 0.1.2, 0.1.3, 0.1.4, 0.1.5, 0.1.6, 1.0.0, 1.1.0, 2.0.0, 2.1.0, 2.2.0, 2.3.0, 2.3.1, 2.4.0, 2.4.1, 2.5.0, 2.5.1, 2.7.0
Source code repository: https://github.com/cran/loo
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.
No records found.
This page was built for software: loo
