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.
|Author||Gordon J. Ross [aut], Dean Markwick [aut, cre], Kees Mulder [ctb] (<https://orcid.org/0000-0002-5387-3812>), Giovanni Sighinolfi [ctb]|
|Maintainer||Dean Markwick <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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