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>.
Version: 1.1.2 Depends: R (≥ 3.5.0) Imports: dplyr (≥ 1.0.5), ggplot2 (≥ 3.2.1), purrr (≥ 0.3.3), rlang (≥ 0.4.10), stats (≥ 3.5.0), stringr (≥ 1.4.0), tidyr (≥ 1.0.0), visNetwork (≥ 2.0.8) Suggests: testthat (≥ 2.3.2) Published: 2022-09-08 DOI: 10.32614/CRAN.package.gmgm Author: Jérémy Roos [aut, cre, cph], RATP Group [fnd, cph] Maintainer: Jérémy Roos <jeremy.roos at gmail.com> License: GPL-3 NeedsCompilation: no Materials: README, NEWS CRAN checks: gmgm results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=gmgm to link to this page.
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