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- Add a new port: math/R-cran-LearnBayes

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LearnBayes contains a collection of functions helpful in learning
  the basic tenets of Bayesian statistical inference. It contains
  functions for summarizing basic one and two parameter posterior
  distributions and predictive distributions. It contains MCMC
  algorithms for summarizing posterior distributions defined by the
  user. It also contains functions for regression models, hierarchical
  models, Bayesian tests, and illustrations of Gibbs sampling.

  WWW: http://cran.r-project.org/web/packages/LearnBayes/

Feature safe:	yes
This commit is contained in:
TAKATSU Tomonari 2012-11-23 02:32:01 +00:00
parent 5dac4c27d1
commit 38a5b84414
Notes: svn2git 2021-03-31 03:12:20 +00:00 
svn path=/head/; revision=307669
4 changed files with 30 additions and 0 deletions
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1
math/Makefile
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@ -8,6 +8,7 @@
SUBDIR += R
SUBDIR += R-cran-Formula
SUBDIR += R-cran-KFAS
SUBDIR += R-cran-LearnBayes
SUBDIR += R-cran-MCMCpack
SUBDIR += R-cran-RSvgDevice
SUBDIR += R-cran-SuppDists

18
math/R-cran-LearnBayes/Makefile Normal file
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@ -0,0 +1,18 @@
# Created by: TAKATSU Tomonari <tota@FreeBSD.org>
# $FreeBSD$
PORTNAME= LearnBayes
PORTVERSION= 2.12
CATEGORIES= math
DISTNAME= ${PORTNAME}_${PORTVERSION}
MAINTAINER= tota@FreeBSD.org
COMMENT= Functions for Learning Bayesian Inference
LICENSE= GPLv2 GPLv3
LICENSE_COMB= dual
USE_R_MOD= yes
R_MOD_AUTOPLIST= yes
.include <bsd.port.mk>

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math/R-cran-LearnBayes/distinfo Normal file
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@ -0,0 +1,2 @@
SHA256 (LearnBayes_2.12.tar.gz) = 5559d5fcceda7b695a62b88b8288a15367ea176b6d8769a8f811f0e9b8a3d37a
SIZE (LearnBayes_2.12.tar.gz) = 88819

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math/R-cran-LearnBayes/pkg-descr Normal file
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LearnBayes contains a collection of functions helpful in learning
the basic tenets of Bayesian statistical inference. It contains
functions for summarizing basic one and two parameter posterior
distributions and predictive distributions. It contains MCMC
algorithms for summarizing posterior distributions defined by the
user. It also contains functions for regression models, hierarchical
models, Bayesian tests, and illustrations of Gibbs sampling.
WWW: http://cran.r-project.org/web/packages/LearnBayes/