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ClustMMDD  

Variable Selection in Clustering by Mixture Models for Discrete Data
View on CRAN: Click here


Download and install ClustMMDD package within the R console
Install from CRAN:
install.packages("ClustMMDD")

Install from Github:
library("remotes")
install_github("cran/ClustMMDD")

Install by package version:
library("remotes")
install_version("ClustMMDD", "1.0.4")



Attach the package and use:
library("ClustMMDD")
Maintained by
Wilson Toussile
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-05-20
Latest Update: 2023-07-07
Description:
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.
How to cite:
Wilson Toussile (2015). ClustMMDD: Variable Selection in Clustering by Mixture Models for Discrete Data. R package version 1.0.4, https://cran.r-project.org/web/packages/ClustMMDD. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.0.0 (2015-05-20 15:19), 1.0.1 (2015-05-23 01:52), 1.0.2 (2016-02-20 16:26), 1.0.3 (2016-02-21 19:51), 1.0.4 (2016-05-30 20:26)
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