R package loo. Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models. Efficient approximate leave-one-out cross-validation (LOO) using 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. We also compute the widely applicable information criterion (WAIC).
Keywords for this software
References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
- Renato Valladares Panaro: spsurv: An R package for semi-parametric survival analysis (2020) arXiv
- Senarathne, S. G. J.; Drovandi, C. C.; McGree, J. M.: A Laplace-based algorithm for Bayesian adaptive design (2020)
- Merkle, Edgar C.; Furr, Daniel; Rabe-Hesketh, Sophia: Bayesian comparison of latent variable models: conditional versus marginal likelihoods (2019)
- van Erp, Sara; Oberski, Daniel L.; Mulder, Joris: Shrinkage priors for Bayesian penalized regression (2019)
- Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
- Massoni, Sébastien; Roux, Nicolas: Optimal group decision: a matter of confidence calibration (2017)
- Paul-Christian Buerkner: Bayesian Distributional Non-Linear Multilevel Modeling with the R Package brms (2017) arXiv
- Paul-Christian Bürkner: brms: An R Package for Bayesian Multilevel Models Using Stan (2017) not zbMATH
- Vehtari, Aki; Gelman, Andrew; Gabry, Jonah: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2017)