BDgraph: Bayesian Structure Learning in Graphical Models using
Birth-Death MCMC
Statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models' literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, and Mohammadi and Wit (2019) <doi:10.18637/jss.v089.i03>.
Version: |
2.68 |
Depends: |
R (≥ 2.10) |
Imports: |
igraph |
Suggests: |
ssgraph, huge, pROC, ggplot2, tmvtnorm, skimr, knitr, rmarkdown |
Published: |
2022-08-08 |
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:
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