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) .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("AdaptGauss")

1.5.4 by Michael Thrun, a month ago


https://www.uni-marburg.de/fb12/datenbionik/software-en


Report a bug at https://github.com/Mthrun/AdaptGauss/issues


Browse source code at https://github.com/cran/AdaptGauss


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] , Alfred Ultsch [aut, cph, ths]


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp, shiny, pracma, methods, ggplot2, DataVisualizations

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

Linking to Rcpp


Imported by DistributionOptimization, ImpactEffectsize, Umatrix.

Suggested by DatabionicSwarm.


See at CRAN