Package: AdaptGauss 1.5.8

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>.

Authors: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], Alfred Ultsch [aut, cph, ths]

AdaptGauss_1.5.8.tar.gz
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AdaptGauss_1.5.8.tgz(r-4.6-x86_64)AdaptGauss_1.5.8.tgz(r-4.6-arm64)AdaptGauss_1.5.8.tgz(r-4.5-x86_64)AdaptGauss_1.5.8.tgz(r-4.5-arm64)
AdaptGauss_1.5.8.tar.gz(r-4.7-arm64)AdaptGauss_1.5.8.tar.gz(r-4.7-x86_64)AdaptGauss_1.5.8.tar.gz(r-4.6-arm64)AdaptGauss_1.5.8.tar.gz(r-4.6-x86_64)
AdaptGauss_1.5.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
AdaptGauss/json (API)

# Install 'AdaptGauss' in R:
install.packages('AdaptGauss', repos = c('https://mthrun.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mthrun/adaptgauss/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

6.12 score 1 stars 5 packages 25 scripts 643 downloads 7 mentions 22 exports 50 dependencies

Last updated from:d0a5d20d90. Checks:11 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR143
linux-devel-x86_64ERROR160
source / vignettesOK222
linux-release-arm64ERROR158
linux-release-x86_64ERROR177
macos-release-arm64ERROR113
macos-release-x86_64ERROR195
macos-oldrel-arm64ERROR152
macos-oldrel-x86_64ERROR280
windows-develERROR112
windows-releaseERROR95
windows-oldrelERROR93
wasm-releaseOK131

Exports:AdaptGaussBayes4MixturesBayesClassificationBayesDecisionBoundariesBayesFor2GMMCDFMixturesChi2testMixturesClassifyByDecisionBoundariesEMGaussGMMplot_ggplot2InformationCriteria4GMMIntersect2MixturesKStestMixturesLikelihoodRatio4MixturesLogLikelihood4MixturesOptimalNoBinsPdf4MixturesPlotMixturesPlotMixturesAndBoundariesQQplotGMMRandomLogGMMSymlognpdf

Dependencies:base64encbslibcachemclicommonmarkcpp11DataVisualizationsdigestfarverfastmapfontawesomefsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrmemoisemimeotelplyrpracmapromisesR6rappdirsRColorBrewerRcppRcppParallelreshape2rlangS7sassscalesshinysourcetoolsspstringistringrvctrsviridisLitewithrxtable

Short Intro into Gaussian Mixture Models

Rendered fromAdaptGauss.Rmdusingknitr::rmarkdownon May 22 2026.

Last update: 2019-11-16
Started: 2019-11-16