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DPP: Inference of Parameters of Normal Distributions from a Mixture of Normals

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

Documentation:

Reference manual: DPP.pdf
Vignettes: Getting started with DPP
DPP Reference Manual

Downloads:

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=DPP 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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