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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1.5.6 by Michael Thrun, 2 years ago

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Authors: Michael Thrun [aut, cre] , Onno Hansen-Goos [aut, rev] , Rabea Griese [ctr, ctb] , Catharina Lippmann [ctr] , Florian Lerch [ctb, rev] , Jorn Lotsch [dtc, rev, fnd, ctb] , Alfred Ultsch [aut, cph, ths]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, shiny, pracma, methods, DataVisualizations

Suggests mclust, grid, foreach, dqrng, parallelDist, knitr, rmarkdown, reshape2, ggplot2

Linking to Rcpp

Imported by DistributionOptimization, Umatrix, opGMMassessment.

Suggested by DatabionicSwarm.

See at CRAN