AdaptGauss: 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) <doi:10.3390/ijms161025897>.

Version: 1.5
Depends: R (≥ 2.10)
Imports: shiny, pracma, methods, ggplot2
Suggests: mclust, grid, foreach
Published: 2019-01-31
Author: Michael Thrun, Onno Hansen-Goos, Rabea Griese, Catharina Lippmann, Florian Lerch, Jorn Lotsch, Alfred Ultsch
Maintainer: Florian Lerch <lerch at>
License: GPL-3
NeedsCompilation: no
CRAN checks: AdaptGauss results


Reference manual: AdaptGauss.pdf
Package source: AdaptGauss_1.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: AdaptGauss_1.5.tgz, r-oldrel: AdaptGauss_1.5.tgz
Old sources: AdaptGauss archive

Reverse dependencies:

Reverse imports: DataVisualizations, DistributionOptimization, Umatrix
Reverse suggests: DatabionicSwarm


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