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>.
| 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:
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