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


AdaptGauss_1.5.8.tar.gz(r-4.4-noble)
AdaptGauss_1.5.8.tgz(r-4.4-emscripten)AdaptGauss_1.5.8.tgz(r-4.3-emscripten)
AdaptGauss.pdf |AdaptGauss.html
AdaptGauss/json (API)

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

Peer review:

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

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

On CRAN:

6.08 score 1 stars 5 packages 23 scripts 470 downloads 7 mentions 22 exports 59 dependencies

Last updated 1 years agofrom:d0a5d20d90. Checks:OK: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024

Exports:AdaptGaussBayes4MixturesBayesClassificationBayesDecisionBoundariesBayesFor2GMMCDFMixturesChi2testMixturesClassifyByDecisionBoundariesEMGaussGMMplot_ggplot2InformationCriteria4GMMIntersect2MixturesKStestMixturesLikelihoodRatio4MixturesLogLikelihood4MixturesOptimalNoBinsPdf4MixturesPlotMixturesPlotMixturesAndBoundariesQQplotGMMRandomLogGMMSymlognpdf

Dependencies:base64encbslibcachemclicolorspacecommonmarkcrayonDataVisualizationsdigestfansifarverfastmapfontawesomefsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigplyrpracmapromisesR6rappdirsRColorBrewerRcppRcppArmadilloreshape2rlangsassscalesshinysourcetoolsspstringistringrtibbleutf8vctrsviridisLitewithrxtable

Short Intro into Gaussian Mixture Models

Rendered fromAdaptGauss.Rmdusingknitr::rmarkdownon Nov 05 2024.

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