Package: SBICgraph 1.0.0

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.

Authors:Quang Nguyen [cre, aut], Jie Zhou [aut], Anne Hoen [aut], Jiang Gui [aut]

SBICgraph_1.0.0.tar.gz
SBICgraph_1.0.0.zip(r-4.5)SBICgraph_1.0.0.zip(r-4.4)SBICgraph_1.0.0.zip(r-4.3)
SBICgraph_1.0.0.tgz(r-4.5-any)SBICgraph_1.0.0.tgz(r-4.4-any)SBICgraph_1.0.0.tgz(r-4.3-any)
SBICgraph_1.0.0.tar.gz(r-4.5-noble)SBICgraph_1.0.0.tar.gz(r-4.4-noble)
SBICgraph_1.0.0.tgz(r-4.4-emscripten)SBICgraph_1.0.0.tgz(r-4.3-emscripten)
SBICgraph.pdf |SBICgraph.html
SBICgraph/json (API)
NEWS

# Install 'SBICgraph' in R:
install.packages('SBICgraph', repos = c('https://qpmnguyen.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 3 scripts 129 downloads 6 exports 25 dependencies

Last updated 4 years agofrom:cc38c4238c. Checks:1 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 05 2025
R-4.5-winNOTEMar 05 2025
R-4.5-macNOTEMar 05 2025
R-4.5-linuxNOTEMar 05 2025
R-4.4-winNOTEMar 05 2025
R-4.4-macNOTEMar 05 2025
R-4.4-linuxNOTEMar 05 2025
R-4.3-winNOTEMar 05 2025
R-4.3-macNOTEMar 05 2025

Exports:comparisonmlemodelsetsbicsggmsimulate

Dependencies:clicodacodetoolsfansiforeachglmnetglueiteratorslatticelifecyclemagrittrMASSMatrixnetworkpillarpkgconfigRcppRcppEigenrlangshapestatnet.commonsurvivaltibbleutf8vctrs

Overview of using SBIC for network models

Rendered fromoverview.Rmdusingknitr::rmarkdownon Mar 05 2025.

Last update: 2021-03-02
Started: 2021-03-02