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: 0.1.1
Depends: methods, Rcpp (≥ 0.12.4), coda
LinkingTo: Rcpp
Published: 2017-10-31
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
Package source: DPP_0.1.1.tar.gz
Windows binaries: r-devel: DPP_0.1.1.zip, r-release: DPP_0.1.1.zip, r-oldrel: DPP_0.1.1.zip
OS X El Capitan binaries: r-release: DPP_0.1.1.tgz
OS X Mavericks binaries: r-oldrel: DPP_0.1.1.tgz
Old sources: DPP archive


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