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: 0.4.2 Depends: R (≥ 2.10) Imports: gtools, ggplot2, mvtnorm Suggests: testthat, knitr, rmarkdown, tidyr, dplyr Published: 2023-08-25 DOI: 10.32614/CRAN.package.dirichletprocess Author: Gordon J. Ross [aut], Dean Markwick [aut, cre], Kees Mulder [ctb], Giovanni Sighinolfi [ctb], Filippo Fiocchi [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, https://dm13450.github.io/dirichletprocess/ NeedsCompilation: no Materials: README NEWS In views: Bayesian CRAN checks: dirichletprocess results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=dirichletprocess to link to this page.
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