mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models

By Luca Scrucca, Michael Fop, Thomas Brendan Murphy, Adrian E. Raftery. The R Journal, 2016

2,399 citations recorded by the source corpus.

Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/eeba30057b0251065a3d3b1da5dc1db7a3843d38

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Citations in the corpus

A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “To assess cell type purity using ARI, the cell type labels were compared against the k - means clustering results using the adjustedRandIndex function of the mclust R package [46].”
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (2017, methodology): “This is the default behavior of the mclust R package [32] and similar to the approach taken by TSCAN, which uses a variable number of principal components inferred from the data.”
The genetic architecture of the human cerebral cortex (2020, methodology): “To identify patterns of genetic correlations of SA and TH (both with and without correction for global measures), we used Mclust(60) for hierarchical cluster analysis, which uses expectation-maximisation to fit parameterized Gaussian mixture models to the data.”
Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer (2018, methodology): “Clustering of tumor samples scored on these five signatures was performed using model-based clustering, using the mclust R package (Scrucca et al., 2016), with the number of clusters, K, determined by maximization of Bayesian Information Criterion (BIC).”
SpikeInterface, a unified framework for spike sorting (2019, background): “XClust [49], MClust [65], SimpleClust [71], Plexon Offline Sorter [7]), but such approaches are nearly impossible to standardize due to inherent operator bias [73].”
The quality of data collected online: An investigation of careless responding in a crowdsourced sample (2020, methodology): “Following the approach by Meade and Craig (2012), we conducted an LPA on the non-self-report indicators of response quality (Open answer quality, response time, IRI, Bogus Item, LongString Index, OEC, RIR, and PTC) using the mclust package for R (Scrucca et al., 2016).”
Gaussian mixture models as a proxy for interacting language models (2025, unknown): “We find that interacting GMMs can replicate the complex time varying dynamics, such as stable and unstable siloing, of interacting LLMs, but with a more easily interpretable underlying algorithm and far less computational cost.”