An R package implementing Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation.
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
You can install the released version of mclust from CRAN using:
Usage of the main functions and several examples are included in the papers shown in the references section below.
For an intro see the vignette A quick tour of mclust, which is available as
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, pp. 205-233.
Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611-631.
Fraley C., Raftery A. E., Murphy T. B. and Scrucca L. (2012) mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.