DPP (0.1.1)

Inference of Parameters of Normal Distributions from a Mixture of Normals.

http://cran.r-project.org/web/packages/DPP

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) we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.

Maintainer: Luis M. Avila
Author(s): Luis M. Avila [aut, cre], Michael R. May [aut], Jeff Ross-Ibarra [aut]

License: MIT + file LICENSE

Uses: coda, Rcpp

Released about 2 years ago.