Implements a fully Bayesian Markov chain Monte Carlo (MCMC) approach for inferring the topology and Boolean logic transition functions of gene regulatory networks from noisy, binary time-series expression data. Network structure and Boolean rules are sampled jointly from their posterior distribution, providing principled uncertainty quantification rather than a single point estimate. Method described in Han et al. (2014) <doi:10.1371/journal.pone.0115806>.
| Version: | 0.1.1 |
| Depends: | R (≥ 4.1.0) |
| Imports: | bitops, stats |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2026-07-15 |
| DOI: | 10.32614/CRAN.package.BBNI (may not be active yet) |
| Author: | Anson Li [aut, cre], Shengtong Han [aut] |
| Maintainer: | Anson Li <liyuanrui618 at gmail.com> |
| BugReports: | https://github.com/anson-li8/BBNI/issues |
| License: | BSD_3_clause + file LICENSE |
| URL: | https://anson-li8.github.io/BBNI/, https://github.com/anson-li8/BBNI |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | BBNI citation info |
| Materials: | README, NEWS |
| CRAN checks: | BBNI results |
| Reference manual: | BBNI.html , BBNI.pdf |
| Vignettes: |
Bayesian Boolean Network Inference with BBNI (source) |
| Package source: | BBNI_0.1.1.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): BBNI_0.1.1.tgz, r-oldrel (arm64): BBNI_0.1.1.tgz, r-release (x86_64): BBNI_0.1.1.tgz, r-oldrel (x86_64): BBNI_0.1.1.tgz |
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