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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 122.9 kB | win-64/r-loo-1.1.0-r3.4.1_0.tar.bz2  7 years and 5 months ago 2309 main cf202003 cf201901
conda 123.3 kB | win-64/r-loo-1.1.0-r3.3.2_0.tar.bz2  7 years and 5 months ago 2431 main cf202003 cf201901
conda 111.1 kB | osx-64/r-loo-1.1.0-r3.4.1_0.tar.bz2  7 years and 5 months ago 876 main cf202003 cf201901
conda 110.7 kB | osx-64/r-loo-1.1.0-r3.3.2_0.tar.bz2  7 years and 5 months ago 932 main cf202003 cf201901
conda 110.7 kB | linux-64/r-loo-1.1.0-r3.3.2_0.tar.bz2  7 years and 5 months ago 5804 main cf202003 cf201901
conda 110.9 kB | linux-64/r-loo-1.1.0-r3.4.1_0.tar.bz2  7 years and 5 months ago 5803 main cf202003 cf201901

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