Bmix: Bayesian Sampling for Stick-Breaking Mixtures

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: 0.5
Depends: mvtnorm
Published: 2015-04-02
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

Downloads:

Reference manual: Bmix.pdf
Package source: Bmix_0.5.tar.gz
Windows binaries: r-devel: Bmix_0.5.zip, r-release: Bmix_0.5.zip, r-oldrel: Bmix_0.5.zip
OS X Snow Leopard binaries: r-release: Bmix_0.5.tgz, r-oldrel: Bmix_0.5.tgz
OS X Mavericks binaries: r-release: Bmix_0.5.tgz
Old sources: Bmix archive