Package: gmgm Type: Package Title: Gaussian Mixture Graphical Model Learning and Inference Version: 1.1.3 Authors@R: c( person("Jérémy", "Roos", email = "jeremy.roos@gmail.com", role = c("aut", "cre", "cph")), person("RATP Group", role = c("fnd", "cph"))) Description: 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) . 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) License: GPL-3 Encoding: UTF-8 LazyData: true RoxygenNote: 7.2.1 Repository: https://jeremyroos.r-universe.dev Date/Publication: 2026-02-25 21:16:18 UTC RemoteUrl: https://github.com/jeremyroos/gmgm RemoteRef: HEAD RemoteSha: 7338abf6a6e51f6691149c67e21b68987409bb62 NeedsCompilation: no Packaged: 2026-10-17 10:09:34 UTC; root Author: Jérémy Roos [aut, cre, cph], RATP Group [fnd, cph] Maintainer: Jérémy Roos