The mixtools package provides functions for analyzing finite mixture models. Parametric and semiparametric mixture models are handled. Various tools are include for determining the number of components. Visualizations include histograms with the estimated mixture model overlaid, 2D and 3D scatterplots for relevant mixture fits, and the mixturegram. More details about the package are included in both the original JSS article as well as a technical report on the open archive HAL.

Other highlights:

• Includes estimation of various parametric and semiparametric mixtures-of-regressions models.

• Functions to help with determining the number of components, including bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria.

• Functions available for estimating Reliability Mixture Models (RMMs).

• Includes a Metropolis-Hastings algorithm for estimating a mixture-of-linear-regressions model.

• Includes some real datasets for which mixture models have been shown to provide good fits.

The JSS article and the technical report on HAL both provide documentation about the mixtools package. Moreover, the help file also documents the references used for each function.

Additional examples for the mixtools package are currently being developed for a Shiny app.

Released and tested versions of mixtools are available via the CRAN network, and can be installed from within R via

install.packages("mixtools")

The issue tickets at the GitHub repo are the primary bug reporting interface. As with the other web resources, previous issues can be searched as well.

Derek S. Young, Tatiana Benaglia, Didier Chauveau, David Hunter, Kedai Cheng, Ryan Elmore, Thomas Hettmansperger, Hoben Thomas, Fengjuan Xua

GPL (>= 2)

This package is based upon work supported by the National Science Foundation, Grant Number SES-0518772 and the Chan Zuckerberg Initiative, Grant Number 2020-225193.

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