diff options
| -rw-r--r-- | math/Makefile | 1 | ||||
| -rw-r--r-- | math/R-cran-LearnBayes/Makefile | 18 | ||||
| -rw-r--r-- | math/R-cran-LearnBayes/distinfo | 2 | ||||
| -rw-r--r-- | math/R-cran-LearnBayes/pkg-descr | 9 |
4 files changed, 30 insertions, 0 deletions
diff --git a/math/Makefile b/math/Makefile index 33f890738f1c..7a48383b1312 100644 --- a/math/Makefile +++ b/math/Makefile @@ -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 diff --git a/math/R-cran-LearnBayes/Makefile b/math/R-cran-LearnBayes/Makefile new file mode 100644 index 000000000000..564b96111134 --- /dev/null +++ b/math/R-cran-LearnBayes/Makefile @@ -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> diff --git a/math/R-cran-LearnBayes/distinfo b/math/R-cran-LearnBayes/distinfo new file mode 100644 index 000000000000..8ab74526ac55 --- /dev/null +++ b/math/R-cran-LearnBayes/distinfo @@ -0,0 +1,2 @@ +SHA256 (LearnBayes_2.12.tar.gz) = 5559d5fcceda7b695a62b88b8288a15367ea176b6d8769a8f811f0e9b8a3d37a +SIZE (LearnBayes_2.12.tar.gz) = 88819 diff --git a/math/R-cran-LearnBayes/pkg-descr b/math/R-cran-LearnBayes/pkg-descr new file mode 100644 index 000000000000..081ac3dfa4a6 --- /dev/null +++ b/math/R-cran-LearnBayes/pkg-descr @@ -0,0 +1,9 @@ +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/ |