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BDgraph: Bayesian Structure Learning in Graphical Models using
Birth-Death MCMC
Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, and Mohammadi et al. (2023) <doi:10.48550/arXiv.2307.00127>.
| Version: |
2.73 |
| Imports: |
igraph, ggplot2, pROC |
| Suggests: |
ssgraph, huge, tmvtnorm, skimr, knitr, rmarkdown |
| Published: |
2024-08-23 |
| DOI: |
10.32614/CRAN.package.BDgraph |
| Author: |
Reza Mohammadi 
[aut, cre],
Ernst Wit [aut],
Adrian Dobra
[ctb] |
| Maintainer: |
Reza Mohammadi <a.mohammadi at uva.nl> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://www.uva.nl/profile/a.mohammadi |
| NeedsCompilation: |
yes |
| Citation: |
BDgraph citation info |
| Materials: |
README NEWS |
| In views: |
Bayesian, GraphicalModels, HighPerformanceComputing, MachineLearning |
| CRAN checks: |
BDgraph results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.