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


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install.packages("DPP")

0.1.2 by Luis M. Avila, a year ago


Browse source code at https://github.com/cran/DPP


Authors: Luis M. Avila [aut, cre] , Michael R. May [aut] , Jeff Ross-Ibarra [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Depends on methods, Rcpp, coda, stats

Suggests R.rsp

Linking to Rcpp


See at CRAN