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: | 0.4.2 |
| 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 |
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