Build Dirichlet Process Objects for Bayesian Modelling R package dirichletprocess version 0.4.2 CRAN Task View: Bayesian (https://CRAN.R-project.org/view=Bayesian) 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. Software Depends: R (>= 2.10) Imports: gtools, ggplot2, mvtnorm Suggests: testthat, knitr, rmarkdown, tidyr, dplyr Dean Markwick <dean.markwick@talk21.com> Comprehensive R Archive Network (CRAN) Gordon J. Ross [aut], Dean Markwick [aut, cre], Kees Mulder [ctb] (ORCID: <https://orcid.org/0000-0002-5387-3812>), Giovanni Sighinolfi [ctb], Filippo Fiocchi [ctb] GPL-3 2023-08-25 application/tgz https://CRAN.R-project.org/package=dirichletprocess doi:10.32614/CRAN.package.dirichletprocess