bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from <>.

Version: 4.9.4
Depends: R (≥ 4.3.0), methods
Suggests: parallel, graph, Rgraphviz, igraph, lattice, gRbase, gRain (≥ 1.3-3), ROCR, Rmpfr, gmp
Published: 2024-05-02
DOI: 10.32614/CRAN.package.bnlearn
Author: Marco Scutari [aut, cre], Tomi Silander [ctb], Robert Ness [ctb]
Maintainer: Marco Scutari <scutari at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: USE_C17
Citation: bnlearn citation info
Materials: ChangeLog
In views: Bayesian, GraphicalModels, HighPerformanceComputing
CRAN checks: bnlearn results


Reference manual: bnlearn.pdf


Package source: bnlearn_4.9.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bnlearn_4.9.4.tgz, r-oldrel (arm64): bnlearn_4.9.4.tgz, r-release (x86_64): bnlearn_4.9.4.tgz, r-oldrel (x86_64): bnlearn_4.9.4.tgz
Old sources: bnlearn archive

Reverse dependencies:

Reverse depends: BNSL, dbnR, GroupBN
Reverse imports: BayesianNetwork, BayesNetBP, bayesvl, bnmonitor, bnpa, BNrich, bnviewer, CBNplot, criticality, dbnlearn, GmicR, imbalance, mDAG, MetNet, MoTBFs, MRPC, OrdCD, pchc, Pigengene, PROPS, r.blip, rPACI, SELF, StratifiedBalancing, TRONCO
Reverse suggests: CompareCausalNetworks, ggkegg, OGI, ParallelPC, rbmn, stagedtrees


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