Package: gmgm 1.1.2
gmgm: Gaussian Mixture Graphical Model Learning and Inference
Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.
Authors:
gmgm_1.1.2.tar.gz
gmgm_1.1.2.zip(r-4.5)gmgm_1.1.2.zip(r-4.4)gmgm_1.1.2.zip(r-4.3)
gmgm_1.1.2.tgz(r-4.4-any)gmgm_1.1.2.tgz(r-4.3-any)
gmgm_1.1.2.tar.gz(r-4.5-noble)gmgm_1.1.2.tar.gz(r-4.4-noble)
gmgm_1.1.2.tgz(r-4.4-emscripten)gmgm_1.1.2.tgz(r-4.3-emscripten)
gmgm.pdf |gmgm.html✨
gmgm/json (API)
NEWS
# Install 'gmgm' in R: |
install.packages('gmgm', repos = c('https://jeremyroos.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jeremyroos/gmgm/issues
- data_air - Beijing air quality dataset
- data_body - NHANES body composition dataset
- gmbn_body - Gaussian mixture Bayesian network learned from the NHANES body composition dataset
- gmdbn_air - Gaussian mixture dynamic Bayesian network learned from the Beijing air quality dataset
- gmm_body - Gaussian mixture model learned from the NHANES body composition dataset
bayesian-networksgaussian-mixture-modelsinferencemachine-learningprobabilistic-graphical-models
Last updated 2 years agofrom:c9ce0c8d35. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 09 2024 |
R-4.5-win | OK | Oct 09 2024 |
R-4.5-linux | OK | Oct 09 2024 |
R-4.4-win | OK | Oct 09 2024 |
R-4.4-mac | OK | Oct 09 2024 |
R-4.3-win | OK | Oct 09 2024 |
R-4.3-mac | OK | Oct 09 2024 |
Exports:add_arcsadd_nodesadd_varaggregationconditionaldensityellipsesemexpectationfilteringgmbngmdbngmminferencemerge_compnetworkparam_emparam_learnparticlespredictionpropagationrelevantremove_arcsremove_nodesremove_varrename_nodesrename_varreordersamplingsmemsmoothingsplit_compstepwisestruct_emstruct_learnstructure
Dependencies:base64encbslibcachemclicolorspacecpp11digestdplyrevaluatefansifarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigpurrrR6rappdirsRColorBrewerrlangrmarkdownsassscalesstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisLitevisNetworkwithrxfunyaml