Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.
mclust, cranlogs, Rmixmod, mixture, EMCluster, mixtools, bgmm, flexmix, igraph, gclus, rrcov, tourr, fpc
Cluster, Multivariate, Distributions, Environmetrics, Graphics, gR, Optimization, Psychometrics, Robust, Spatial
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For attribution, please cite this work as
Scrucca, et al., "The R Journal: mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models", The R Journal, 2016
BibTeX citation
@article{RJ-2016-021,
author = {Scrucca, Luca and Fop, Michael and Murphy, T. Brendan and Raftery, Adrian E.},
title = {The R Journal: mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-021},
doi = {10.32614/RJ-2016-021},
volume = {8},
issue = {1},
issn = {2073-4859},
pages = {289-317}
}