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author | wen <wen@FreeBSD.org> | 2011-03-07 20:04:35 +0800 |
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committer | wen <wen@FreeBSD.org> | 2011-03-07 20:04:35 +0800 |
commit | 0841bc7906a96181aea8c221024a09a669667eff (patch) | |
tree | 255d29f1c85703efae6c05d1fb186b9ba3ec3650 /science | |
parent | b6f7153ee21fe920bc889ace8f1eb21c9b71f6c8 (diff) | |
download | freebsd-ports-gnome-0841bc7906a96181aea8c221024a09a669667eff.tar.gz freebsd-ports-gnome-0841bc7906a96181aea8c221024a09a669667eff.tar.zst freebsd-ports-gnome-0841bc7906a96181aea8c221024a09a669667eff.zip |
bayesm 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, and Analysis of Multivariate Ordinal survey
data with scale usage heterogeneity (as in Rossi et al, JASA (01)).
WWW: http://www.perossi.org/home/bsm-1
Diffstat (limited to 'science')
-rw-r--r-- | science/Makefile | 1 | ||||
-rw-r--r-- | science/R-cran-bayesm/Makefile | 20 | ||||
-rw-r--r-- | science/R-cran-bayesm/distinfo | 2 | ||||
-rw-r--r-- | science/R-cran-bayesm/pkg-descr | 16 |
4 files changed, 39 insertions, 0 deletions
diff --git a/science/Makefile b/science/Makefile index 7fddc0c0642e..0e190b2b4bba 100644 --- a/science/Makefile +++ b/science/Makefile @@ -7,6 +7,7 @@ SUBDIR += 2dhf SUBDIR += InsightToolkit SUBDIR += R-cran-AMORE + SUBDIR += R-cran-bayesm SUBDIR += abinit SUBDIR += afni SUBDIR += at diff --git a/science/R-cran-bayesm/Makefile b/science/R-cran-bayesm/Makefile new file mode 100644 index 000000000000..7665ad0f9fe7 --- /dev/null +++ b/science/R-cran-bayesm/Makefile @@ -0,0 +1,20 @@ +# New ports collection makefile for: R-cran-bayesm +# Date created: March 07, 2011 +# Whom: Wen Heping <wenheping@gmail.com> +# +# $FreeBSD$ +# + +PORTNAME= bayesm +PORTVERSION= 2.2.4 +CATEGORIES= science +PKGNAMEPREFIX= R-cran- +DISTNAME= ${PORTNAME}_${PORTVERSION:C/\./-/g:C/-/\./1} + +MAINTAINER= wen@FreeBSD.org +COMMENT= Bayesian Inference for Marketing/Micro-econometrics + +USE_R_MOD= yes +R_MOD_AUTOPLIST= yes + +.include <bsd.port.mk> diff --git a/science/R-cran-bayesm/distinfo b/science/R-cran-bayesm/distinfo new file mode 100644 index 000000000000..ddf8de6c5a85 --- /dev/null +++ b/science/R-cran-bayesm/distinfo @@ -0,0 +1,2 @@ +SHA256 (bayesm_2.2-4.tar.gz) = 93bfcd6652106c159fa4bc12552d34dcfee7a28597c8bf64f8ca7af65b834ce4 +SIZE (bayesm_2.2-4.tar.gz) = 1766401 diff --git a/science/R-cran-bayesm/pkg-descr b/science/R-cran-bayesm/pkg-descr new file mode 100644 index 000000000000..681ddd86a291 --- /dev/null +++ b/science/R-cran-bayesm/pkg-descr @@ -0,0 +1,16 @@ +bayesm 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, and Analysis of Multivariate Ordinal survey +data with scale usage heterogeneity (as in Rossi et al, JASA (01)). + +WWW: http://www.perossi.org/home/bsm-1 |