# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "BiDAG" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'BiDAG: Bayesian Inference for Directed Acyclic Graphs'
version: 2.1.4
doi: 10.18637/jss.v105.i09
identifiers:
- type: doi
  value: 10.32614/CRAN.package.BiDAG
abstract: 'Implementation of a collection of MCMC methods for Bayesian structure learning
  of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient
  inference on larger DAGs, the space of DAGs is pruned according to the data. To
  filter the search space, the algorithm employs a hybrid approach, combining constraint-based
  learning with search and score. A reduced search space is initially defined on the
  basis of a skeleton obtained by means of the PC-algorithm, and then iteratively
  improved with search and score. Search and score is then performed following two
  approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous
  data and the BDe score is implemented for binary data or categorical data. The algorithms
  may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs)
  from the posterior distribution given the data. All algorithms are also applicable
  for structure learning and sampling for dynamic Bayesian networks. References: J.
  Kuipers, P. Suter, G. Moffa (2022) <https://doi.org/10.1080/10618600.2021.2020127>,
  N. Friedman and D. Koller (2003) <https://doi.org/10.1023/A:1020249912095>, J. Kuipers
  and G. Moffa (2017) <https://doi.org/10.1080/01621459.2015.1133426>, M. Kalisch
  et al. (2012) <https://doi.org/10.18637/jss.v047.i11>, D. Geiger and D. Heckerman
  (2002) <https://doi.org/10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa,
  N.Beerenwinkel (2023) <https://doi.org/10.18637/jss.v105.i09>.'
authors:
- family-names: Suter
  given-names: Polina
  email: polina.suter@gmail.com
- family-names: Kuipers
  given-names: Jack
preferred-citation:
  type: article
  title: Bayesian Structure Learning and Sampling of Bayesian Networks with the R
    Package BiDAG
  authors:
  - family-names: Suter
    given-names: Polina
    email: polina.suter@gmail.com
  - family-names: Kuipers
    given-names: Jack
    email: jack.kuipers@bsse.ethz.ch
  - family-names: Moffa
    given-names: Giusi
    email: giusi.moffa@unibas.ch
  - family-names: Beerenwinkel
    given-names: Niko
    email: niko.beerenwinkel@bsse.ethz.ch
  journal: Journal of Statistical Software
  year: '2023'
  volume: '105'
  issue: '9'
  doi: 10.18637/jss.v105.i09
  start: '1'
  end: '31'
repository: https://polinasuter.r-universe.dev
commit: 6097d8ea159f5e4dc3c95b6d815d8e827efd5532
date-released: '2023-05-16'
contact:
- family-names: Suter
  given-names: Polina
  email: polina.suter@gmail.com