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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.
Getting started
Browse package contents
Package details |
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| Author | Gordon J. Ross [aut], Dean Markwick [aut, cre], Kees Mulder [ctb] (<https://orcid.org/0000-0002-5387-3812>), Giovanni Sighinolfi [ctb], Filippo Fiocchi [ctb] |
| Maintainer | Dean Markwick <dean.markwick@talk21.com> |
| License | GPL-3 |
| Version | 0.4.2 |
| URL | https://github.com/dm13450/dirichletprocess https://dm13450.github.io/dirichletprocess/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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