CRAN/E | DPP

DPP

Inference of Parameters of Normal Distributions from a Mixture of Normals

Installation

About

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.

Key Metrics

Version 0.1.2
Published 2018-05-24 2157 days ago
Needs compilation? yes
License MIT
License File
CRAN checks DPP results

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Maintainer

Maintainer

Luis M. Avila

lmavila@gmail.com

Authors

Luis M. Avila

aut / cre

Michael R. May

aut

Jeff Ross-Ibarra

aut

Material

Reference manual
Package source

Vignettes

Getting started with DPP
DPP Reference Manual

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

DPP archive

Depends

methods
Rcpp ≥ 0.12.4
coda
stats

Suggests

R.rsp

LinkingTo

Rcpp