This is a bare-bones implementation of sampling algorithms for a variety of Bayesian stick-breaking (marginally DP) mixture models, including particle learning and Gibbs sampling for static DP mixtures, particle learning for dynamic BAR stick-breaking, and DP mixture regression. The software is designed to be easy to customize to suit different situations and for experimentation with stick-breaking models. Since particles are repeatedly copied, it is not an especially efficient implementation.
| Version: | 0.6 |
| Depends: | mvtnorm |
| Published: | 2016-02-07 |
| Author: | Matt Taddy |
| Maintainer: | Matt Taddy <taddy at chicagobooth.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://faculty.chicagobooth.edu/matt.taddy |
| NeedsCompilation: | yes |
| In views: | Bayesian, Cluster |
| CRAN checks: | Bmix results |
| Reference manual: | Bmix.pdf |
| Package source: | Bmix_0.6.tar.gz |
| Windows binaries: | r-devel: Bmix_0.6.zip, r-release: Bmix_0.6.zip, r-oldrel: Bmix_0.6.zip |
| macOS binaries: | r-release: Bmix_0.6.tgz, r-oldrel: Bmix_0.6.tgz |
| Old sources: | Bmix archive |
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