mclustAddons-package {mclustAddons} | R Documentation |
Addons for the mclust package
Description
An R package extending the functionality of the mclust package (Scrucca et al. 2916, 2023) for Gaussian finite mixture modeling by including:
density estimation for data with bounded support (Scrucca, 2019)
modal clustering using MEM algorithm for Gaussian mixtures (Scrucca, 2021)
entropy estimation via Gaussian mixture modeling (Robin & Scrucca, 2023)
For a quick introduction to mclustAddons see the vignette A quick tour of mclustAddons.
Author(s)
Maintainer: Luca Scrucca luca.scrucca@unipg.it (ORCID) [copyright holder]
References
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. doi: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. doi:10.1201/9781003277965
Scrucca L. (2019) A transformation-based approach to Gaussian mixture density estimation for bounded data. Biometrical Journal, 61:4, 873–888. doi: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. doi:10.1002/sam.11527
Robin S. and Scrucca L. (2023) Mixture-based estimation of entropy. Computational Statistics & Data Analysis, 177, 107582. doi:10.1016/j.csda.2022.107582
See Also
densityMclustBounded()
for density estimation of bounded data;
MclustMEM()
for modal clustering;
EntropyGMM()
for entropy estimation.