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: |
0.4.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
gtools, ggplot2, mvtnorm |
| Suggests: |
testthat, knitr, rmarkdown, tidyr, dplyr |
| Published: |
2020-06-13 |
| Author: |
Gordon J. Ross [aut],
Dean Markwick [aut, cre],
Kees Mulder  [ctb],
Giovanni Sighinolfi [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 |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| In views: |
Bayesian |
| CRAN checks: |
dirichletprocess results |
Documentation:
Downloads:
Reverse dependencies:
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