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, a year 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.

Suggested by DatabionicSwarm.

See at CRAN