Gaussian Mixture Models (GMM)

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) .


Reference manual

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1.5 by Florian Lerch, 8 months ago

Browse source code at

Authors: Michael Thrun , Onno Hansen-Goos , Rabea Griese , Catharina Lippmann , Florian Lerch , Jorn Lotsch , Alfred Ultsch

Documentation:   PDF Manual  

GPL-3 license

Imports shiny, pracma, methods, ggplot2

Suggests mclust, grid, foreach

Imported by DistributionOptimization, Umatrix.

Suggested by DataVisualizations, DatabionicSwarm.

See at CRAN