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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:	1.6
Depends:	R (≥ 2.10)
Imports:	Rcpp,shiny,pracma, methods,DataVisualizations,plotly
LinkingTo:	Rcpp
Suggests:	mclust, grid,foreach,dqrng,parallelDist,knitr (≥ 1.12),rmarkdown (≥ 0.9),reshape2,ggplot2
Published:	2024-02-02
DOI:	10.32614/CRAN.package.AdaptGauss
Author:	Michael Thrun [aut, cre], Onno Hansen-Goos [aut, rev], Rabea Griese [ctr, ctb], Catharina Lippmann [ctr], Florian Lerch [ctb, rev], Quirin Stier [ctb, rev], Jorn Lotsch [dtc, rev, fnd, ctb], Luca Brinkmann [ctb, rev], Alfred Ultsch [aut, cph, ths]
Maintainer:	Michael Thrun <m.thrun at gmx.net>
BugReports:	https://github.com/Mthrun/AdaptGauss/issues
License:	GPL-3
URL:	https://www.deepbionics.org
NeedsCompilation:	yes
CRAN checks:	AdaptGauss results

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