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BiDAG

2.1.4Package record

Bayesian Inference for Directed Acyclic Graphs

2packages depend
6.4Kdownloads / year
–test coverage
9/13flavors OK

Overview

About

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) doi:10.1080/10618600.2021.2020127, N. Friedman and D. Koller (2003) doi:10.1023/A:1020249912095, J. Kuipers and G. Moffa (2017) doi:10.1080/01621459.2015.1133426, M. Kalisch et al. (2012) doi:10.18637/jss.v047.i11, D. Geiger and D. Heckerman (2002) doi:10.1214/aos/1035844981, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) doi:10.18637/jss.v105.i09.

Install

Health

CRAN checks
4NOTE9OK
Check messages
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Polina Suter <polina.suter@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = "Polina",
                      family = "Suter",
                      role = c("aut", "cre"),
                      email = "polina.suter@gmail.com"),
               person(given = "Jack",
                      family = "Kuipers",
                      role = "aut"))
as necessary.
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespace in Imports field not imported from:
  ‘RBGL’
All declared Imports should be used.

CRAN check page ↗

Slowest check: 4.5 min · r-devel-windows-x86_64
Status changes
  • NOTE9 OK, 4 NOTENow
  • NOTE10 OK, 4 NOTE2026-04-22
  • ERROR9 OK, 4 NOTE, 1 ERROR2026-04-18
  • NOTE10 OK, 4 NOTE2026-03-10
Advisories

none known

Documentation

In release 2.1.4
READMENoVignettesNoNEWSNoCitation fileYes · cites a publicationWebsitenot declared in DESCRIPTION
Cites a JSS article
Help pages
54
Examples that run
80%28 run in a plain check · 7 never run
Exported functions with examples
95%
Documented parameters
97%
Return-value docs
97%
Help pages citing references
28%

Downloads

6.4K
CRAN downloads in the past year
Rank #4,258 · ~17/day · ~531/mo
Daily download trend is not available in this view yet.
85830 days
2.1K90 days
6.4K1 year
Compare downloads with other packages →
Also on426 r2u

Dependencies

Declared dependencies
7 external dependencies (excludes base and recommended)

Installing BiDAG adds 28 packages (16 with compiled code, 36 MB of source tarballs). Heaviest: BH 14 MB, igraph 4.8 MB, pcalg 3.9 MB. 3 more not on CRAN are not measured.

Depends (1)
R >= 3.5.0
Imports (9)
RcppmethodsgraphRgraphvizRBGLpcalggraphicsMatrixcoda
LinkingTo (1)
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
2direct
0indirect
Imports 2
Depends, Imports or LinkingTo, most downloaded first

Datasets

People & History

People (from the free-text Author field) (2)
Author, Maintainer · added in 1.0.2

Maintainer since 1.0.0 (2017)

Listed in earlier versions (1)
Polina Minkina
no longer listed · 1.0.0 to 1.0.1
Releases (23)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 2.1.4Latest
    record
    2023-05-16 · current release · diff ↗
  • R
    R 4.3.0 released · 2023-04-21
  • 2.1.3
    record
    2023-01-23 · diff ↗
  • 2.1.2
    record
    2022-12-21 · diff ↗ ·
    added 1 export
    getRuntime
  • 2.1.1
    record
    2022-08-05 · diff ↗
  • 2.1.0
    record
    2022-06-27 · diff ↗
  • 2.0.9
    record
    2022-06-20 · diff ↗
  • 2.0.7
    record
    2022-05-16 · diff ↗
  • 2.0.6
    record
    2022-05-09 · diff ↗ ·
    added 4 exports
    bidag2coda, bidag2codalist, learnBN, sampleBN
  • R
    R 4.2.0 released · 2022-04-22
  • 2.0.5
    record
    2022-04-13 · diff ↗
  • 2.0.4
    record
    2021-11-30 · diff ↗ ·
    added 1 export
    scoreagainstDBN
  • 2.0.3
    record
    2021-07-28 · diff ↗ ·
    removed 1 export
    plotdiffs.DBN
    added 5 exports
    getDAG, getMCMCscore, getSpace, getTrace, plotdiffsDBN
Show 17 earlier eventsHide 17 earlier events
  • R
    R 4.1.0 released · 2021-05-18
  • 2.0.2
    record
    2021-04-30 · diff ↗
  • 2.0.1
    record
    2021-04-28 · diff ↗ ·
    removed 12 exports
    iterations.check, plot.MCMCmult, plot.MCMCres, print.MCMCmult, print.MCMCres, print.MCMCscoretab, print.scoreparameters, sample.check, summary.MCMCmult, summary.MCMCres, summary.MCMCscoretab, summary.scoreparameters
    added 4 exports
    itercomp, plot2in1, samplecomp, scorespace
  • 2.0.0
    record
    2021-02-15 · diff ↗ ·
    removed 2 exports
    dag.threshold, edges.posterior
    added 15 exports
    connectedSubGraph, edgep, getSubGraph, modelp, plot.MCMCmult, plot.MCMCres, print.MCMCmult, print.MCMCres, print.MCMCscoretab, print.scoreparameters, string2mat, summary.MCMCmult, summary.MCMCres, summary.MCMCscoretab, summary.scoreparameters
  • 1.4.1
    record
    2020-07-14 · diff ↗ ·
    added 2 exports
    plotdiffs, plotdiffs.DBN
  • R
    R 4.0.0 released · 2020-04-24
  • 1.3.4
    record
    2020-04-12 · diff ↗ ·
    removed 2 exports
    plotdiffs, plotdiffs.DBN
  • 1.3.0
    record
    2020-02-19 · diff ↗ ·
    removed 4 exports
    adjacency2dag, dag2adjacencymatrix, dag2skeletonadjacency, iterativeMCMCsearch
    added 12 exports
    DBNscore, compact2full, compareDBNs, full2compact, graph2m, iterativeMCMC, m2graph, plotDBN, plotdiffs, plotdiffs.DBN, plotpcor, plotpedges
  • 1.2.0
    record
    2019-08-19 · diff ↗
  • R
    R 3.6.0 released · 2019-04-26
  • 1.1.2
    record
    2018-05-24 · diff ↗
  • R
    R 3.5.0 released · 2018-04-23
  • 1.1.1
    record
    2018-03-16 · diff ↗
  • 1.0.2
    record
    2017-09-08 · diff ↗
  • 1.0.1
    record
    2017-07-26 · diff ↗
  • 1.0.0
    record
    2017-07-19
  • R
    R 3.4.0 released · 2017-04-21

Package metadata

First published
2017-07-19
Total releases
23 / 9 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 3.5.0
Bundled data
915 KB / 13 files
Download size
1.0 MB

Cite

Cite this package

Run in R for the authors' entry:

citation("BiDAG")

The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.18637/jss.v105.i09.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for BiDAG version 2.1.4 [Data set]. HJJB, LLC. Data release v2026-10-26. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-26, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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