mclust (version 6.0.1)

mclust-package: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

Description

Gaussian finite mixture models estimated via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization and dimension reduction.

Author

Chris Fraley, Adrian Raftery and Luca Scrucca.

Maintainer: Luca Scrucca

Details

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

See also:

• Mclust for clustering;

• MclustDA for supervised classification;

• MclustSSC for semi-supervised classification;

• densityMclust for density estimation.

References

Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman & Hall/CRC, ISBN: 978-1032234953, https://mclust-org.github.io/book/

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. 289-317.

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.

Examples

# \donttest{
# 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)
# }