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:
SBICgraph_1.0.0.tar.gz
SBICgraph_1.0.0.zip(r-4.7-any)SBICgraph_1.0.0.zip(r-4.6-any)SBICgraph_1.0.0.zip(r-4.5-any)
SBICgraph_1.0.0.tgz(r-4.6-any)SBICgraph_1.0.0.tgz(r-4.5-any)
SBICgraph_1.0.0.tar.gz(r-4.7-any)SBICgraph_1.0.0.tar.gz(r-4.6-any)
SBICgraph_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SBICgraph/json (API)
| # Install 'SBICgraph' in R: |
| install.packages('SBICgraph', repos = c('https://qpmnguyen.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:cc38c4238c. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | NOTE | 158 | ||
| source / vignettes | OK | 188 | ||
| linux-release | NOTE | 118 | ||
| macos-release | NOTE | 161 | ||
| macos-oldrel | NOTE | 188 | ||
| windows-devel | NOTE | 96 | ||
| windows-release | NOTE | 92 | ||
| windows-oldrel | NOTE | 118 | ||
| wasm-release | OK | 86 |
Exports:comparisonmlemodelsetsbicsggmsimulate
Dependencies:clicodacodetoolsforeachglmnetglueiteratorslatticelifecyclemagrittrMASSMatrixnetworkpillarpkgconfigRcppRcppEigenrlangshapestatnet.commonsurvivaltibbleutf8vctrs
