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.
- Version1.1.2
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release09/08/2022
Documentation
Team
Jérémy Roos
RATP Group
Show author detailsRolesfnd, Copyright holder
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports7 packages
- Suggests1 package