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.

Package details

AuthorGordon J. Ross [aut], Dean Markwick [aut, cre], Kees Mulder [ctb] (<https://orcid.org/0000-0002-5387-3812>)
MaintainerDean Markwick <[email protected]>
LicenseGPL-3
Version0.3.1
URL https://github.com/dm13450/dirichletprocess
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("dirichletprocess")

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dirichletprocess documentation built on Jan. 11, 2020, 9:16 a.m.