Package: opGMMassessment 0.4

Jorn Lotsch

opGMMassessment: Optimized Automated Gaussian Mixture Assessment

Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) <doi:10.1016/j.imu.2022.101113>.

Authors:Jorn Lotsch [aut,cre], Sebastian Malkusch [aut], Martin Maechler [ctb], Peter Rousseeuw [ctb], Anja Struyf [ctb], Mia Hubert [ctb], Kurt Hornik [ctb]

opGMMassessment_0.4.tar.gz
opGMMassessment_0.4.tar.gz(r-4.5-noble)opGMMassessment_0.4.tar.gz(r-4.4-noble)
opGMMassessment_0.4.tgz(r-4.4-emscripten)opGMMassessment_0.4.tgz(r-4.3-emscripten)
opGMMassessment.pdf |opGMMassessment.html✨
opGMMassessment/json (API)

# Install 'opGMMassessment' in R:
install.packages('opGMMassessment', repos = 'https://cloud.r-project.org')
Datasets:
  • Chromatogram - Example data of lysophosphatidic acids, LPA.
  • Mixture3 - Example Gaussian mixture data.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.48 score 1 packages 153 downloads 2 exports 120 dependencies

Last updated 12 months agofrom:902173ad22. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 27 2025
R-4.5-linuxOKMar 27 2025
R-4.4-linuxOKMar 27 2025

Exports:GMMplotGGopGMMassessment

Dependencies:AdaptGaussaskpassbase64encbitopsbootbslibcachemcaToolscliclusterClusterRcodacodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tableDataVisualizationsdigestdiptestDistributionOptimizationdoParalleldplyrevaluatefansifarverfastGHQuadfastmapFNNfontawesomeforeachfsGAgenericsggplot2gluegmpgtablehighrhtmltoolshtmlwidgetshttpuvhttrisobanditeratorsjquerylibjsonlitekernlabKernSmoothknitrkslabelinglaterlatticelazyevallifecyclelme4magrittrMASSMatrixmclustmemoisemgcvmimeminqamixAKmixtoolsmnormtmulticoolmultimodemunsellmvtnormNbClustnlmenloptropensslpillarpkgconfigplotlyplyrpracmapromisespurrrR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rlangrmarkdownrootSolvesassscalessegmentedshinysourcetoolsspstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtableyaml

Citation

Jörn Lötsch, Sebastian Malkusch, Alfred Ultsch. Comparative assessment of automated algorithms for the separation of one-dimensional Gaussian mixtures. Informatics in Medicine Unlocked, Volume 34, 2022 https://doi.org/10.1016/j.imu.2022.101113 https://www.sciencedirect.com/science/article/pi/S2352914822002507)

Corresponding BibTeX entry: 

  @Article{,
    title = {Comparative assessment of automated algorithms for the
      separation of one-dimensional Gaussian mixtures},
    journal = {Informatics in Medicine Unlocked},
    volume = {34},
    pages = {101113},
    year = {2022},
    issn = {2352-9148},
    doi = {10.1016/j.imu.2022.101113},
    url =
      {https://www.sciencedirect.com/science/article/pii/S2352914822002507},
    author = {Jörn Lötsch and Sebastian Malkusch and Alfred Ultsch},
  }