bnlearn: Bayesian Network Structure Learning, Parameter Learning and
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/>.
||R (≥ 4.2.0), methods
||parallel, graph, Rgraphviz, igraph, lattice, gRbase, gRain (≥ 1.3-3), ROCR, Rmpfr, gmp
||Marco Scutari [aut, cre], Tomi Silander [ctb], Robert Ness [ctb]
||Marco Scutari <scutari at bnlearn.com>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
||bnlearn citation info
||Bayesian, GraphicalModels, HighPerformanceComputing
||BNSL, dbnR, GroupBN
||BayesianNetwork, BayesNetBP, bayesvl, bnmonitor, bnpa, BNrich, bnviewer, CBNplot, criticality, dbnlearn, GmicR, imbalance, IntOMICS, mDAG, MetNet, MoTBFs, MRPC, OrdCD, pchc, Pigengene, PROPS, r.blip, rPACI, SELF, StratifiedBalancing, TRONCO
||CompareCausalNetworks, ggkegg, mcmcabn, OGI, ParallelPC, rbmn, sparsebnUtils, stagedtrees
Please use the canonical form
to link to this page.