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-4.4-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)
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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

6 exports 0.00 score 25 dependencies 3 scripts 131 downloads

Last updated 4 years agofrom:cc38c4238c. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:comparisonmlemodelsetsbicsggmsimulate

Dependencies:clicodacodetoolsfansiforeachglmnetglueiteratorslatticelifecyclemagrittrMASSMatrixnetworkpillarpkgconfigRcppRcppEigenrlangshapestatnet.commonsurvivaltibbleutf8vctrs