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.
Package details |
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Author | Matt Taddy <taddy@chicagobooth.edu> |
Maintainer | Matt Taddy <taddy@chicagobooth.edu> |
License | GPL (>= 2) |
Version | 0.6 |
URL | http://faculty.chicagobooth.edu/matt.taddy |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
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