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Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo. 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.

copied from cf-staging / r-loo
Type Size Name Uploaded Downloads Labels
conda 1.8 MB | noarch/r-loo-2.6.0-r43hc72bb7e_2.conda  5 months and 11 days ago 706 main
conda 1.8 MB | noarch/r-loo-2.6.0-r44hc72bb7e_2.conda  5 months and 11 days ago 502 main
conda 1.8 MB | noarch/r-loo-2.6.0-r43hc72bb7e_1.conda  1 year and 5 months ago 3425 main
conda 1.8 MB | noarch/r-loo-2.6.0-r42hc72bb7e_1.conda  1 year and 5 months ago 3483 main
conda 1.8 MB | noarch/r-loo-2.6.0-r42hc72bb7e_0.conda  1 year and 8 months ago 1766 main
conda 1.8 MB | noarch/r-loo-2.6.0-r41hc72bb7e_0.conda  1 year and 8 months ago 2399 main

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