bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC and RSMAX2) 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 and cross-validation. Development snapshots with the latest bugfixes are available from

Version: 4.0
Depends: R (≥ 2.14.0), methods
Suggests: parallel, graph, Rgraphviz, lattice, gRain
Published: 2016-05-16
Author: Marco Scutari
Maintainer: Marco Scutari <marco.scutari at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: bnlearn citation info
Materials: ChangeLog
In views: Bayesian, HighPerformanceComputing, gR
CRAN checks: bnlearn results


Reference manual: bnlearn.pdf
Package source: bnlearn_4.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: bnlearn_4.0.tgz, r-oldrel: bnlearn_4.0.tgz
Old sources: bnlearn archive

Reverse dependencies:

Reverse depends: geneNetBP
Reverse imports: BayesianNetwork, CovSelHigh, MoTBFs
Reverse suggests: BNDataGenerator, BTR, ParallelPC, rbmn, sparsebnUtils


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