dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

Perform nonparametric Bayesian analysis using Dirichlet processes without the need to program the inference algorithms. Utilise included pre-built models or specify custom models and allow the 'dirichletprocess' package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, among many other sources.

Version: 0.2.2
Depends: R (≥ 2.10)
Imports: gtools, ggplot2, mvtnorm
Suggests: testthat, knitr, rmarkdown, tidyr, dplyr
Published: 2018-11-23
Author: Gordon J. Ross [aut], Dean Markwick [aut, cre], Kees Mulder ORCID iD [ctb]
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_0.2.2.tar.gz
Windows binaries: r-devel: dirichletprocess_0.2.2.zip, r-release: dirichletprocess_0.2.2.zip, r-oldrel: dirichletprocess_0.2.2.zip
OS X binaries: r-release: dirichletprocess_0.2.2.tgz, r-oldrel: dirichletprocess_0.2.2.tgz
Old sources: dirichletprocess archive


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