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add version 3.1-4 to r-bayesm (#20807)

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Glenn Johnson 2021-01-15 03:47:14 -06:00 committed by GitHub
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var/spack/repos/builtin/packages/r-bayesm/package.py
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@ -7,12 +7,33 @@
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')