dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

Create Dirichlet process objects that can be used as infinite mixture models in a variety of ways. Some examples include; density estimation, Poisson process intensity inference, hierarchical modelling and clustering. See Teh, Y. W. (2011) <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, among many other sources.

Version: 0.2.0
Depends: R (≥ 2.10)
Imports: gtools, ggplot2, mvtnorm
Suggests: testthat, knitr, rmarkdown, tidyr, dplyr
Published: 2018-01-29
Author: Dean Markwick [aut, cre], Gordon J. Ross [aut]
Maintainer: Dean Markwick <dean.markwick at talk21.com>
BugReports: https://github.com/dm13450/dirichletprocess/issues
License: GPL-3
URL: https://github.com/dm13450/dirichletprocess
NeedsCompilation: no
Materials: README NEWS
CRAN checks: dirichletprocess results


Reference manual: dirichletprocess.pdf
Vignettes: dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models
Package source: dirichletprocess_0.2.0.tar.gz
Windows binaries: r-devel: dirichletprocess_0.2.0.zip, r-release: dirichletprocess_0.2.0.zip, r-oldrel: dirichletprocess_0.2.0.zip
OS X El Capitan binaries: r-release: dirichletprocess_0.2.0.tgz
OS X Mavericks binaries: r-oldrel: not available


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