Inference of Parameters of Normal Distributions from a Mixture of Normals R package DPP version 0.1.2 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. Software Depends: methods, Rcpp (>= 0.12.4), coda, stats LinkingTo: Rcpp Suggests: R.rsp Luis M. Avila <lmavila@gmail.com> Comprehensive R Archive Network (CRAN) Luis M. Avila [aut, cre], Michael R. May [aut], Jeff Ross-Ibarra [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=DPP/LICENSE) 2018-05-24 application/tgz https://CRAN.R-project.org/package=DPP doi:10.32614/CRAN.package.DPP