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) <>.


Reference manual

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1.1.0 by Jérémy Roos, 2 months ago

Browse source code at

Authors: Jérémy Roos [aut, cre, cph] , RATP Group [fnd, cph]

Documentation:   PDF Manual  

GPL-3 license

Imports dplyr, ggplot2, purrr, rlang, stats, stringr, tidyr, visNetwork

Suggests testthat

See at CRAN