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), 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 <>.


Reference manual

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4.4.1 by Marco Scutari, a month ago

Browse source code at

Authors: Marco Scutari [aut, cre] , Robert Ness [ctb]

Documentation:   PDF Manual  

Task views: Bayesian Inference, High-Performance and Parallel Computing with R, gRaphical Models in R

GPL (>= 2) license

Depends on methods

Suggests parallel, graph, Rgraphviz, lattice, gRain, ROCR, Rmpfr, gmp

Imported by BayesNetBP, BayesianNetwork, CovSelHigh, MRPC, SELF, bnviewer, imbalance, mDAG, miic, mvgraphnorm, r.blip, wiseR.

Depended on by BNSL, geneNetBP.

Suggested by BNDataGenerator, BTR, CompareCausalNetworks, OGI, ParallelPC, mcmcabn, rbmn, sparsebnUtils.

See at CRAN