Bayesian Inference for Directed Acyclic Graphs
Overview
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
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Polina Suter <polina.suter@gmail.com>’
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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
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- NOTE9 OK, 4 NOTENow
- NOTE10 OK, 4 NOTE2026-04-22
- ERROR9 OK, 4 NOTE, 1 ERROR2026-04-18
- NOTE10 OK, 4 NOTE2026-03-10
Documentation
- 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
Dependencies
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.
Code & Tests
Datasets
People & History
Maintainer since 1.0.0 (2017)
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 2.1.4Latest
- RR 4.3.0 released · 2023-04-21
- 2.1.32023-01-23 · diff ↗
- 2.1.2
- 2.1.12022-08-05 · diff ↗
- 2.1.02022-06-27 · diff ↗
- 2.0.92022-06-20 · diff ↗
- 2.0.72022-05-16 · diff ↗
- 2.0.6
- RR 4.2.0 released · 2022-04-22
- 2.0.52022-04-13 · diff ↗
- 2.0.4
- 2.0.32021-07-28 · diff ↗ ·
removed 1 export
plotdiffs.DBNadded 5 exports
getDAG, getMCMCscore, getSpace, getTrace, plotdiffsDBN
Show 17 earlier eventsHide 17 earlier events
- RR 4.1.0 released · 2021-05-18
- 2.0.22021-04-30 · diff ↗
- 2.0.12021-04-28 · 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.scoreparametersadded 4 exports
itercomp, plot2in1, samplecomp, scorespace - 2.0.02021-02-15 · diff ↗ ·
removed 2 exports
dag.threshold, edges.posterioradded 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
- RR 4.0.0 released · 2020-04-24
- 1.3.4
- 1.3.02020-02-19 · diff ↗ ·
removed 4 exports
adjacency2dag, dag2adjacencymatrix, dag2skeletonadjacency, iterativeMCMCsearchadded 12 exports
DBNscore, compact2full, compareDBNs, full2compact, graph2m, iterativeMCMC, m2graph, plotDBN, plotdiffs, plotdiffs.DBN, plotpcor, plotpedges - 1.2.02019-08-19 · diff ↗
- RR 3.6.0 released · 2019-04-26
- 1.1.22018-05-24 · diff ↗
- RR 3.5.0 released · 2018-04-23
- 1.1.12018-03-16 · diff ↗
- 1.0.22017-09-08 · diff ↗
- 1.0.12017-07-26 · diff ↗
- 1.0.02017-07-19
- RR 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.
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