bayesGDS: Functions to implement Generalized Direct Sampling

This package contains functions to help users implement the Generalized Direct Sampling algorithm for Bayesian hierarchical models (Braun and Damien, 2013). GDS is useful for sampling from posterior distributions for which there is a large number of conditionally independent heterogeneous units.

Version: 0.6.0
Depends: R (≥ 3.0.2), Matrix (≥ 1.1.0), compiler
Suggests: sparseHessianFD (≥ 0.1.1), sparseMVN (≥ 0.1.0), mvtnorm, trustOptim (≥ 0.8.3), plyr (≥ 1.8)
Published: 2013-12-14
Author: Michael Braun
Maintainer: Michael Braun <braunm at smu.edu>
License: MPL (== 2.0)
URL: www.cox.smu.edu/web/michaelbraun
NeedsCompilation: no
Materials: NEWS
CRAN checks: bayesGDS results

Downloads:

Reference manual: bayesGDS.pdf
Vignettes: Using the bayesGDS package
Package source: bayesGDS_0.6.0.tar.gz
MacOS X binary: bayesGDS_0.6.0.tgz
Windows binary: bayesGDS_0.6.0.zip
Old sources: bayesGDS archive