ClustMMDD: Variable Selection in Clustering by Mixture Models for Discrete Data

An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.

Package details

AuthorWilson Toussile
MaintainerWilson Toussile <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the ClustMMDD package in your browser

Any scripts or data that you put into this service are public.

ClustMMDD documentation built on May 2, 2019, 2:44 p.m.