Package: bayesGDS
Type: Package
Title: Scalable Rejection Sampling for Bayesian Hierarchical Models
Version: 0.6.2
Date: 2016-03-16
Authors@R: person(family="Braun", given="Michael", role=c("aut","cre","cph"), email="braunm@smu.edu")
URL: coxprofs.cox.smu.edu/braunm
Maintainer: Michael Braun <braunm@smu.edu>
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 (>= 3.2.4), Matrix (>= 1.2.4)
Suggests: sparseHessianFD(>= 0.3.0), sparseMVN(>= 0.2.0), mvtnorm,
        trustOptim (>= 0.8.5), plyr (>= 1.8), dplyr, testthat, knitr,
        R.rsp, MCMCpack
VignetteBuilder: R.rsp
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-03-16 16:52:45 UTC; braunm
Author: Michael Braun [aut, cre, cph]
Repository: CRAN
Date/Publication: 2016-03-16 18:37:03
