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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 ORCID iD [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

Documentation:

Reference manual: AdaptGauss.pdf
Vignettes: Short Intro into Gaussian Mixture Models

Downloads:

Package source: AdaptGauss_1.6.tar.gz
Windows binaries: r-devel: AdaptGauss_1.6.zip, r-release: AdaptGauss_1.6.zip, r-oldrel: AdaptGauss_1.6.zip
macOS binaries: r-release (arm64): AdaptGauss_1.6.tgz, r-oldrel (arm64): AdaptGauss_1.6.tgz, r-release (x86_64): AdaptGauss_1.6.tgz, r-oldrel (x86_64): AdaptGauss_1.6.tgz
Old sources: AdaptGauss archive

Reverse dependencies:

Reverse imports: DistributionOptimization, opGMMassessment, scapGNN, Umatrix

Linking:

Please use the canonical form https://CRAN.R-project.org/package=AdaptGauss to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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