mixtools: An R Package for Analyzing Mixture Models

By Tatiana Benaglia, Didier Chauveau, David R. Hunter, Derek S. Young. 2009

1,081 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Mechanics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6b162def7a07bffaf9f0f72cbba536283a634439

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David R. Hunter
2. Michael Levine
3. Didier Chauveau
4. Michael Schneider
5. Sohrab P. Shah
6. Jake McMullen
7. Derek S. Young
8. Sourav Chatterjee

In Computational Mechanics

1. Didier Chauveau
2. David R. Hunter

In Statistics and Probability

1. Michael Levine
2. Didier Chauveau
3. David R. Hunter
4. Michael Schneider
5. Jake McMullen
6. Cun-Hui Zhang
7. Steven M. Goodreau
8. Mark S. Handcock

Citations in the corpus

• Maximum smoothed likelihood for multivariate mixtures (2011, methodology): “The current article corrects this shortcoming by introducing a smoothed loglikelihood function and formulating an iterative algorithm with a provable monotonicity property that happens to produce results in that resemble those of Benaglia et al. (2009b) in practice.”
• The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes (2016, positive): “…this classification for samples which had gene expression data available, we fit a two-component Gaussian mixture model to the expression levels of ESR1 using the mixtools package 59 in R, and computed the probabilities of the samples belonging to the two distributions defined by the components.”
• Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis (2017, methodology): “…(Vermunt, 1997) x x 1-step, LD poLCA (Linzer & Lewis, 2011, 2013) x 1-step MCLUST (Fraley et al., 2012) x DepMixS4 (Visser & Speekenbrink, 2010) x x x 1-step, time-intensive data mixtools (Benaglia et al., 2009) x non-parametric models Bayes/LCA (White & Murphy, 2014) x Bayesian estimation Note.”