Package: DPP
Type: Package
Title: Inference of Parameters of Normal Distributions from a Mixture
        of Normals
Version: 0.1.2
Authors@R: c(
    person("Luis M.", "Avila", email = "lmavila@gmail.com", role = c("aut","cre")),
    person("Michael R.", "May", email = "mikeryanmay@ucdavis.edu", role = "aut"),
    person("Jeff", "Ross-Ibarra", email = "rossibarra@ucdavis.edu", role = "aut"))
Description: 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.
License: MIT + file LICENSE
Depends: methods, Rcpp (>= 0.12.4), coda, stats
Suggests: R.rsp
VignetteBuilder: R.rsp
LinkingTo: Rcpp
RcppModules: DPPmcmc,Models
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-05-24 06:17:07 UTC; lavila
Author: Luis M. Avila [aut, cre],
  Michael R. May [aut],
  Jeff Ross-Ibarra [aut]
Maintainer: Luis M. Avila <lmavila@gmail.com>
Repository: CRAN
Date/Publication: 2018-05-24 08:38:30 UTC
Built: R 3.4.4; x86_64-w64-mingw32; 2019-04-25 18:51:05 UTC; windows
Archs: i386, x64
