Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Models
c(person("Aki", "Vehtari", email = "Aki.Vehtari@aalto.fi", role = c("aut")),
             person("Jonah", "Gabry", email = "jsg2201@columbia.edu", role = c("cre", "aut")),
             person("Mans", "Magnusson", role = c("aut")),
             person("Yuling", "Yao", role = c("aut")),
             person("Paul-Christian", "Bürkner", role = c("aut")),
             person("Topi", "Paananen", role = c("aut")),
             person("Andrew", "Gelman", role = c("aut")),
             person("Ben", "Goodrich", role = c("ctb")),
             person("Juho", "Piironen", role = c("ctb")), 
             person("Bruno", "Nicenboim", role = c("ctb")), 
             person("Leevi", "Lindgren", role = c("ctb")))
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.
checkmate, matrixStats (>= 0.52), parallel, stats
bayesplot (>= 1.7.0), brms (>= 2.10.0), ggplot2, graphics,
knitr, posterior, rmarkdown, rstan, rstanarm (>= 2.19.0),
rstantools, spdep, testthat (>= 2.1.0)
Aki Vehtari [aut],
  Jonah Gabry [cre, aut],
  Mans Magnusson [aut],
  Yuling Yao [aut],
  Paul-Christian Bürkner [aut],
  Topi Paananen [aut],
  Andrew Gelman [aut],
  Ben Goodrich [ctb],
  Juho Piironen [ctb],
  Bruno Nicenboim [ctb],
  Leevi Lindgren [ctb]