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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.
| Version: |
0.4.2 |
| Depends: |
R (≥ 2.10) |
| Imports: |
gtools, ggplot2, mvtnorm |
| Suggests: |
testthat, knitr, rmarkdown, tidyr, dplyr |
| Published: |
2023-08-25 |
| 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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