SBICgraph: Structural Bayesian Information Criterion for Graphical Models

This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review). In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.

Version: 1.0.0
Imports: glmnet, MASS, network
Suggests: knitr, rmarkdown
Published: 2021-03-02
Author: Quang Nguyen ORCID iD [cre, aut], Jie Zhou [aut], Anne Hoen [aut], Jiang Gui [aut]
Maintainer: Quang Nguyen <Quang.P.Nguyen.GR at dartmouth.edu>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: SBICgraph results

Documentation:

Reference manual: SBICgraph.pdf
Vignettes: overview

Downloads:

Package source: SBICgraph_1.0.0.tar.gz
Windows binaries: r-devel: SBICgraph_1.0.0.zip, r-release: SBICgraph_1.0.0.zip, r-oldrel: SBICgraph_1.0.0.zip
macOS binaries: r-release (arm64): SBICgraph_1.0.0.tgz, r-release (x86_64): SBICgraph_1.0.0.tgz, r-oldrel: SBICgraph_1.0.0.tgz

Linking:

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