Mixtools: An R package for analyzing finite mixture models

Tatiana Benaglia, Didier Chauveau, David R. Hunter, Derek S. Young

Research output: Contribution to journalArticlepeer-review

938 Scopus citations


The mixtools package for R provides a set of functions for analyzing a variety of finite mixture models. These functions include both traditional methods, such as EM algorithms for univariate and multivariate normal mixtures, and newer methods that reflect some recent research in finite mixture models. In the latter category, mixtools provides algorithms for estimating parameters in a wide range of different mixture-of-regression contexts, in multinomial mixtures such as those arising from discretizing continuous multivariate data, in nonparametric situations where the multivariate component densities are completely unspecified, and in semiparametric situations such as a univariate location mixture of symmetric but otherwise unspecified densities. Many of the algorithms of the mixtools package are EM algorithms or are based on EM-like ideas, so this article includes an overview of EM algorithms for finite mixture models.

Original languageEnglish
Pages (from-to)1-29
Number of pages29
JournalJournal of Statistical Software
Issue number6
StatePublished - 2009


  • Cutpoint
  • EM algorithm
  • Mixture of regressions
  • Model-based clustering
  • Nonparametric mixture
  • Semiparametric mixture
  • Unsupervised clustering

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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