bayesGDS: Scalable Rejection Sampling for Bayesian Hierarchical Models

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.

Version: 0.6.1
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
Published: 2015-03-31
Author: Michael Braun [aut, cre, cph]
Maintainer: Michael Braun <braunm at smu.edu>
License: MPL (== 2.0)
URL: coxprofs.cox.smu.edu/braunm
NeedsCompilation: no
Citation: bayesGDS citation info
Materials: NEWS
CRAN checks: bayesGDS results

Downloads:

Reference manual: bayesGDS.pdf
Vignettes: Estimating Bayesian Hierarchical Models using bayesGDS
Small test example 1
Small test example 2
Package source: bayesGDS_0.6.1.tar.gz
Windows binaries: r-devel: bayesGDS_0.6.1.zip, r-release: bayesGDS_0.6.1.zip, r-oldrel: bayesGDS_0.6.1.zip
OS X Snow Leopard binaries: r-release: bayesGDS_0.6.0.tgz, r-oldrel: bayesGDS_0.6.0.tgz
OS X Mavericks binaries: r-release: bayesGDS_0.6.1.tgz
Old sources: bayesGDS archive