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 and Wit (2019) <doi:10.18637/jss.v089.i03>.

Version: 2.64
Imports: igraph
Suggests: ssgraph, huge, pROC, ggplot2, tmvtnorm
Published: 2021-05-03
Author: Reza Mohammadi ORCID iD [aut, cre], Ernst Wit ORCID iD [aut], Adrian Dobra ORCID iD [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, HighPerformanceComputing, MachineLearning, gR
CRAN checks: BDgraph results

Downloads:

Reference manual: BDgraph.pdf
Vignettes: An Introduction to the BDgraph Package for Bayesian Graphical Models
Package source: BDgraph_2.64.tar.gz
Windows binaries: r-devel: BDgraph_2.64.zip, r-release: BDgraph_2.64.zip, r-oldrel: BDgraph_2.64.zip
macOS binaries: r-release: BDgraph_2.64.tgz, r-oldrel: BDgraph_2.64.tgz
Old sources: BDgraph archive

Reverse dependencies:

Reverse depends: ssgraph
Reverse imports: bmixture, bootnet
Reverse suggests: BayesSUR, qgraph

Linking:

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