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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  1 year and 1 month ago 1266 main
conda 1.8 MB | noarch/r-loo-2.6.0-r44hc72bb7e_2.conda  1 year and 1 month ago 1028 main
conda 1.8 MB | noarch/r-loo-2.6.0-r43hc72bb7e_1.conda  2 years and 2 months ago 3965 main
conda 1.8 MB | noarch/r-loo-2.6.0-r42hc72bb7e_1.conda  2 years and 2 months ago 4569 main
conda 1.8 MB | noarch/r-loo-2.6.0-r42hc72bb7e_0.conda  2 years and 4 months ago 2305 main
conda 1.8 MB | noarch/r-loo-2.6.0-r41hc72bb7e_0.conda  2 years and 4 months ago 3164 main

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