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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "BBNI" in publications use:'
type: software
license: BSD-3-Clause
title: 'BBNI: Bayesian Inference of Boolean Genetic Networks'
version: 0.1.1
doi: 10.1371/journal.pone.0115806
abstract: 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) <https://doi.org/10.1371/journal.pone.0115806>.
authors:
- family-names: Li
  given-names: Anson
  email: liyuanrui618@gmail.com
- family-names: Han
  given-names: Shengtong
  email: shengtong.han@marquette.edu
preferred-citation:
  type: article
  title: A Full Bayesian Approach for Boolean Genetic Network Inference
  authors:
  - family-names: Han
    given-names: Shengtong
    email: shengtong.han@marquette.edu
  - family-names: Wong
    given-names: Raymond K. W.
  - family-names: Lee
    given-names: Thomas C. M.
  - family-names: Shen
    given-names: Linghao
  - family-names: Li
    given-names: Shuo-Yen R.
  - family-names: Fan
    given-names: Xiaodan
  journal: PLoS ONE
  year: '2014'
  volume: '9'
  issue: '12'
  doi: 10.1371/journal.pone.0115806
  start: e115806
repository: https://cran.r-universe.dev
repository-code: https://github.com/anson-li8/BBNI
commit: 751b38427aefa99ba7e22a06f1f6b401a6a55b71
url: https://anson-li8.github.io/BBNI/
date-released: '2026-07-15'
contact:
- family-names: Li
  given-names: Anson
  email: liyuanrui618@gmail.com