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雷达卡
Tatiana Benaglia/Pennsylvania State University
Didier Chauveau/Universit′e d’Orl′eans
David R. Hunter/Pennsylvania State University
Derek S. Young/Pennsylvania State University
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 algo-
rithms 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 mul-
tivariate 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.
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