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This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.
| Version: | 0.1.2 |
| Depends: | methods, Rcpp (≥ 0.12.4), coda, stats |
| LinkingTo: | Rcpp |
| Suggests: | R.rsp |
| Published: | 2018-05-24 |
| DOI: | 10.32614/CRAN.package.DPP |
| Author: | Luis M. Avila [aut, cre], Michael R. May [aut], Jeff Ross-Ibarra [aut] |
| Maintainer: | Luis M. Avila <lmavila at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| CRAN checks: | DPP results |
| Reference manual: | DPP.pdf |
| Vignettes: |
Getting started with DPP DPP Reference Manual |
| Package source: | DPP_0.1.2.tar.gz |
| Windows binaries: | r-devel: DPP_0.1.2.zip, r-release: DPP_0.1.2.zip, r-oldrel: DPP_0.1.2.zip |
| macOS binaries: | r-release (arm64): DPP_0.1.2.tgz, r-oldrel (arm64): DPP_0.1.2.tgz, r-release (x86_64): DPP_0.1.2.tgz, r-oldrel (x86_64): DPP_0.1.2.tgz |
| Old sources: | DPP archive |
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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.