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.

Maintainer: Michael Braun <braunm at smu.edu>

Author(s): Michael Braun*

Install package and any missing dependencies by running this line in your R console:

install.packages("bayesGDS")

Depends R (>= 3.2.4), Matrix(>=1.2.4)
Imports
Suggests sparseHessianFD(>=0.3.0), sparseMVN(>=0.2.0), mvtnorm, trustOptim(>=0.8.5), plyr(>=1.8), dplyr, testthat, knitr, R.rsp, MCMCpack
Enhances
Linking to
Reverse
depends
Reverse
imports
Reverse
suggests
Reverse
enhances
Reverse
linking to

Package bayesGDS
Materials
URL coxprofs.cox.smu.edu/braunm
Task Views
Version 0.6.2
Published 2016-03-16
License MPL (== 2.0)
BugReports
SystemRequirements
NeedsCompilation no
Citation
CRAN checks bayesGDS check results
Package source bayesGDS_0.6.2.tar.gz