diff options
| -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 |