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 <https://www.bnlearn.com/>.
Version: 5.0.2 Depends: R (≥ 4.4.0), methods Suggests: parallel, graph, Rgraphviz, igraph, lattice, gRbase, gRain (≥ 1.3-3), Rmpfr, gmp Published: 2025-01-07 DOI: 10.32614/CRAN.package.bnlearn Author: Marco Scutari [aut, cre], Tomi Silander [ctb] Maintainer: Marco Scutari <scutari at bnlearn.com> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] URL: https://www.bnlearn.com/ NeedsCompilation: yes SystemRequirements: USE_C17 Citation: bnlearn citation info Materials: ChangeLog In views: Bayesian, GraphicalModels, HighPerformanceComputing CRAN checks: bnlearn results Documentation: Downloads: Reverse dependencies: Reverse depends: BNSL, dbnR, GroupBN Reverse imports: BayesianNetwork, BayesNetBP, bayesvl, bnmonitor, bnpa, bnRep, BNrich, bnviewer, CBNplot, cia, criticality, dbnlearn, GmicR, imbalance, mDAG, MetNet, MoTBFs, MRPC, OrdCD, pchc, Pigengene, PROPS, r.blip, rPACI, SELF, StratifiedBalancing, TRONCO Reverse suggests: CompareCausalNetworks, dsld, ggkegg, gRain, OGI, ParallelPC, rbmn, stagedtrees Linking:Please use the canonical form https://CRAN.R-project.org/package=bnlearn to link to this page.
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