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 .

Documentation

Manual: bnlearn.pdf
Vignette: None available.

Maintainer: Marco Scutari <marco.scutari at gmail.com>

Author(s): Marco Scutari

Install package and any missing dependencies by running this line in your R console:

install.packages("bnlearn")

Depends R (>= 2.14.0), methods
Imports
Suggests parallel, graph, Rgraphviz, lattice, gRain
Enhances
Linking to
Reverse
depends
BNSL, geneNetBP
Reverse
imports
BayesianNetwork, CovSelHigh, MoTBFs
Reverse
suggests
BNDataGenerator, BTR, ParallelPC, rbmn, sparsebnUtils
Reverse
enhances
Reverse
linking to

Package bnlearn
Materials
URL http://www.bnlearn.com/
Task Views Bayesian , HighPerformanceComputing , gR
Version 4.1.1
Published 2017-03-26
License GPL (>= 2)
BugReports
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks bnlearn check results
Package source bnlearn_4.1.1.tar.gz