The R Journal: article published in 2016, volume 8:1

mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
Luca Scrucca, Michael Fop, T. Brendan Murphy and Adrian E. Raftery , The R Journal (2016) 8:1, pages 289-317.

Abstract 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.

Received: 2015-11-11; online 2016-06-13
CRAN packages: mclust, cranlogs, Rmixmod, mixture, EMCluster, mixtools, bgmm, flexmix, igraph, gclus, rrcov, tourr, fpc
CRAN Task Views implied by cited CRAN packages: Cluster, Multivariate, Distributions, Environmetrics, Graphics, gR, Optimization, Psychometrics, Robust, Spatial

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2016-021,
  author = {Luca Scrucca and Michael Fop and T. Brendan Murphy and
          Adrian E. Raftery},
  title = {{mclust 5: Clustering, Classification and Density Estimation
          Using Gaussian Finite Mixture Models}},
  year = {2016},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2016-021},
  url = {https://doi.org/10.32614/RJ-2016-021},
  pages = {289--317},
  volume = {8},
  number = {1}
}