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
This package has been downloaded 210 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 4 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 3,146 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 34 downloads.
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