bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference (original) (raw)
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: | 5.0.1 |
|---|---|
| Depends: | R (≥ 4.4.0), methods |
| Suggests: | parallel, graph, Rgraphviz, igraph, lattice, gRbase, gRain (≥ 1.3-3), Rmpfr, gmp |
| Published: | 2024-08-19 |
| DOI: | 10.32614/CRAN.package.bnlearn |
| Author: | Marco Scutari [aut, cre], Tomi Silander [ctb] |
| Maintainer: | Marco Scutari |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://www.bnlearn.com/ |
| NeedsCompilation: | yes |
| SystemRequirements: | USE_C17 |
| Citation: | bnlearn citation info |
| Materials: | |
| 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, r.blip, rPACI, SELF, StratifiedBalancing, TRONCO |
| Reverse suggests: | CompareCausalNetworks, dsld, ggkegg, gRain, OGI, ParallelPC, rbmn, stagedtrees |
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=bnlearnto link to this page.