mclustAddons-package: Addons for the *mclust* package

In mclustAddons: Addons for the 'mclust' Package

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 [e-mail] (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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("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. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1002/sam.11527")}

Robin S. and Scrucca L. (2023) Mixture-based estimation of entropy. Computational Statistics & Data Analysis, 177, 107582. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1016/j.csda.2022.107582")}

See Also

densityMclustBounded() for density estimation of bounded data; MclustMEM() for modal clustering; EntropyGMM() for entropy estimation.

mclustAddons documentation built on April 3, 2025, 11:19 p.m.