Structural Bayesian Information Criterion for Graphical Models
Overview
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.
Minor changes
- Bump major version to 1.0.0
- Adjusted vignettes to reset
parback to user choice
From NEWS.md in the release
Install
Health
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespace in Imports field not imported from: ‘network’ All declared Imports should be used.
- NOTE11 OK, 2 NOTE2026-09-08
- ERROR11 OK, 1 NOTE, 1 ERROR2026-09-07
- NOTE9 OK, 5 NOTE2026-03-10
Documentation
- Help pages
- 8
- Examples that run
- 60%3 run in a plain check · 2 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 83%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
- overview HTML
Downloads
Dependencies
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.0.0Latest2021-03-02 · current release
- RR 4.0.0 released · 2020-04-24
Package metadata
- First published
- 2021-03-02
- Total releases
- 1 / 5 yrs
- License
- GPL-3 OSI
- Download size
- 26 KB
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