Package: bayesGDS
Type: Package
Title: Scalable Rejection Sampling for Bayesian Hierarchical Models
Version: 0.6.1
Date: 2015-03-30
Authors@R: person(family="Braun", given="Michael", role=c("aut","cre","cph"), email="braunm@smu.edu")
URL: coxprofs.cox.smu.edu/braunm
Description: Functions for implementing the Braun and Damien (2015) rejection sampling algorithm for Bayesian hierarchical models.  The algorithm generates posterior samples in parallel, and is scalable when the individual units are conditionally independent.
License: MPL (== 2.0)
Depends: R (>= 3.1.2), Matrix (>= 1.1.5)
Suggests: sparseHessianFD(>= 0.2.0), sparseMVN(>= 0.2.0), mvtnorm,
        trustOptim (>= 0.8.5), plyr (>= 1.8), dplyr, testthat, knitr,
        R.rsp, MCMCpack
VignetteBuilder: R.rsp
NeedsCompilation: no
Packaged: 2015-03-30 19:56:02 UTC; braunm
Author: Michael Braun [aut, cre, cph]
Maintainer: Michael Braun <braunm@smu.edu>
Repository: CRAN
Date/Publication: 2015-03-31 07:49:38
