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authorGlenn Johnson <glenn-johnson@uiowa.edu>2021-01-17 11:33:55 -0600
committerGitHub <noreply@github.com>2021-01-17 11:33:55 -0600
commite19464979f61b0179c05a5b2156222e644d7bb5d (patch)
treeb78e1bbc9a49052722590cbfd24e7866b4144b48
parent6606eb6a9610f1d42df4f7ebec45df00d6eb7cf3 (diff)
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add version 2.4.1 to r-loo (#21081)
-rw-r--r--var/spack/repos/builtin/packages/r-loo/package.py15
1 files changed, 13 insertions, 2 deletions
diff --git a/var/spack/repos/builtin/packages/r-loo/package.py b/var/spack/repos/builtin/packages/r-loo/package.py
index 08ed68ca2d..a693c93441 100644
--- a/var/spack/repos/builtin/packages/r-loo/package.py
+++ b/var/spack/repos/builtin/packages/r-loo/package.py
@@ -7,13 +7,24 @@ from spack import *
class RLoo(RPackage):
- """loo: Efficient Leave-One-Out Cross-Validation and WAIC for
- BayesianModels"""
+ """Efficient Leave-One-Out Cross-Validation and WAIC for BayesianModels
+
+ 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."""
homepage = "https://mc-stan.org/loo"
url = "https://cloud.r-project.org/src/contrib/loo_2.1.0.tar.gz"
list_url = "https://cloud.r-project.org/src/contrib/Archive/loo"
+ version('2.4.1', sha256='bc21fb6b4a93a7e95ee1be57e4e787d731895fb8b4743c26b30b43adee475b50')
version('2.3.1', sha256='d98de21b71d9d9386131ae5ba4da051362c3ad39e0305af4f33d830f299ae08b')
version('2.1.0', sha256='1bf4a1ef85d151577ff96d4cf2a29c9ef24370b0b1eb08c70dcf45884350e87d')