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: | 0.6.2 |
| 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 |
| Published: | 2016-03-16 |
| 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 |
| Reference manual: | bayesGDS.pdf |
| Vignettes: |
Estimating Bayesian Hierarchical Models using bayesGDS Small test example 1 Small test example 2 |
| Package source: | bayesGDS_0.6.2.tar.gz |
| Windows binaries: | r-devel: bayesGDS_0.6.2.zip, r-release: bayesGDS_0.6.2.zip, r-oldrel: bayesGDS_0.6.2.zip |
| OS X binaries: | r-release: bayesGDS_0.6.2.tgz, r-oldrel: bayesGDS_0.6.2.tgz |
| Old sources: | bayesGDS archive |
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