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