mclust (version 5.0.2)

mclust-package: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation

Description

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

References

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (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. C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611:631.

Details

For a quick introduction to mclust see the vignette ../doc/mclust.html{A quick tour of mclust}.

Examples

# 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)