mclust-package: Gaussian Mixture Modelling for Model-Based Clustering,...

Description Details Author(s) References Examples

Description

Finite Gaussian mixture modelling fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization and dimension reduction.

Details

For a quick introduction to mclust see the vignette A quick tour of mclust.

Author(s)

Chris Fraley, Adrian Raftery and Luca Scrucca.

Maintainer: Luca Scrucca [email protected]

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, 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.

Examples

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# Clustering
mod1 <- Mclust(iris[,1:4])
summary(mod1)
plot(mod1,  what = c("BIC", "classification"))

# Classification
data(banknote)
mod2 <- MclustDA(banknote[,2:7], banknote$Status)
summary(mod2)
plot(mod2)

# Density estimation
mod3 <- densityMclust(faithful$waiting)
summary(mod3)
plot(mod3, faithful$waiting)

mclust documentation built on July 2, 2018, 9:03 a.m.