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authorGlenn Johnson <glenn-johnson@uiowa.edu>2021-01-15 03:47:14 -0600
committerGitHub <noreply@github.com>2021-01-15 10:47:14 +0100
commit8599480ed93a0117f326689280c7a896d6bf697a (patch)
tree6ca2bf8cc78a2194cf51c9bca4cf0cc3f361a361
parent13d0618f039220ef6bf672eadcfb10e0cc7eb1c5 (diff)
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add version 3.1-4 to r-bayesm (#20807)
-rw-r--r--var/spack/repos/builtin/packages/r-bayesm/package.py23
1 files changed, 22 insertions, 1 deletions
diff --git a/var/spack/repos/builtin/packages/r-bayesm/package.py b/var/spack/repos/builtin/packages/r-bayesm/package.py
index b4923e820e..ac7d1de5a4 100644
--- a/var/spack/repos/builtin/packages/r-bayesm/package.py
+++ b/var/spack/repos/builtin/packages/r-bayesm/package.py
@@ -7,12 +7,33 @@ from spack import *
class RBayesm(RPackage):
- """Bayesian Inference for Marketing/Micro-Econometrics"""
+ """Bayesian Inference for Marketing/Micro-Econometrics
+
+ Covers many important models used in marketing and micro-econometrics
+ applications. The package includes: Bayes Regression (univariate or
+ multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary
+ and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP),
+ Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate
+ Mixtures of Normals (including clustering), Dirichlet Process Prior Density
+ Estimation with normal base, Hierarchical Linear Models with normal prior
+ and covariates, Hierarchical Linear Models with a mixture of normals prior
+ and covariates, Hierarchical Multinomial Logits with a mixture of normals
+ prior and covariates, Hierarchical Multinomial Logits with a Dirichlet
+ Process prior and covariates, Hierarchical Negative Binomial Regression
+ Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment
+ of linear instrumental variables models, Analysis of Multivariate Ordinal
+ survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)),
+ Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP
+ (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book,
+ Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley
+ 2005) and Bayesian Non- and Semi-Parametric Methods and Applications
+ (Princeton U Press 2014)."""
homepage = "https://cloud.r-project.org/package=bayesm"
url = "https://cloud.r-project.org/src/contrib/bayesm_3.1-0.1.tar.gz"
list_url = "https://cloud.r-project.org/src/contrib/Archive/bayesm"
+ version('3.1-4', sha256='061b216c62bc72eab8d646ad4075f2f78823f9913344a781fa53ea7cf4a48f94')
version('3.1-3', sha256='51e4827eca8cd4cf3626f3c2282543df7c392b3ffb843f4bfb386fe104642a10')
version('3.1-2', sha256='a332f16e998ab10b17a2b1b9838d61660c36e914fe4d2e388a59f031d52ad736')
version('3.1-1', sha256='4854517dec30ab7c994de862aae1998c2d0c5e71265fd9eb7ed36891d4676078')