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An R package extending the functionality of the mclust package (Scrucca et al. 2016, 2023) for Gaussian finite mixture modeling by including:
density estimation for data with bounded support using a transform-based approach to Gaussian mixture density estimation (Scrucca, 2019);
modal clustering using modal EM algorithm for Gaussian mixtures (Scrucca, 2021);
entropy estimation via Gaussian mixture modeling (Robin & Scrucca, 2023).
You can install the released version of mclustAddons from CRAN using:
install.packages("mclustAddons")For an introduction to the main functions and several examples see the vignette A quick tour of mclustAddons, which is available as
vignette("mclustAddons")The vignette is also available in the Vignette section on the navigation bar on top of the package’s web page.
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, 205-233. https://doi.org/10.32614/RJ-2016-021
Scrucca L., Fraley C., Murphy T.B., Raftery A.E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman and Hall/CRC. https://doi.org/10.1201/9781003277965
Scrucca L. (2019) A transformation-based approach to Gaussian mixture density estimation for bounded data, Biometrical Journal, 61:4, 873–888. https://doi.org/10.1002/bimj.201800174
Scrucca L. (2021) A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314. https://doi.org/10.1002/sam.11527
Robin S. and Scrucca L. (2023) Mixture-based estimation of entropy. Computational Statistics & Data Analysis, 177, 107582. https://doi.org/10.1016/j.csda.2022.107582
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.