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bnlearn (version 5.2.1)

Bayesian Network Structure Learning, Parameter Learning and Inference

Description

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing, Tabu Search, DirectLiNGAM) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian, conditional Gaussian and zero-inflated 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, interventions, counterfactuals, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from .

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Version

Install

install.packages('bnlearn')

Monthly Downloads

39,518

Version

5.2.1

License

GPL (>= 2)

Maintainer

Marco Scutari

Last Published

July 17th, 2026

Functions in bnlearn (5.2.1)

The bn.fit class structure

bn.strength class

The bn.strength class structure

Nonparametric bootstrap of Bayesian networks

bnlearn-package

Bayesian network structure learning, parameter learning and inference

Plot fitted Bayesian networks

causal inference

Perform causal inference

bn.fit utilities

Utilities to manipulate fitted Bayesian networks

The bn.kcv class structure

Fit the parameters of a Bayesian network

causal discovery algorithms

Causal discovery algorithms

Coronary heart disease data set

constraint-based algorithms

Constraint-based structure learning algorithms

clgaussian.test

Synthetic (mixed) data set to test learning algorithms

graph enumeration

Count graphs with specific characteristics

independence-tests

Conditional independence tests

Construct configurations of discrete variables

Perform conditional probability queries

Compare two or more different Bayesian networks

Independence and conditional independence tests

Equivalence classes, moral graphs and consistent extensions

Plotting networks with parameter summaries

graph utilities

Utilities to manipulate graphs

foreign files utilities

Read and write BIF, NET, DSC and DOT files

Synthetic (continuous) data set to test learning algorithms

graph generation utilities

Generate empty, complete or random graphs

Advanced Bayesian network plots

graph integration

Import and export networks from the graph package

gRain integration

Import and export networks from the gRain package

The HailFinder weather forecast system (synthetic) data set

Test d-separation

hybrid algorithms

Hybrid structure learning algorithms

score-based algorithms

Score-based structure learning algorithms

information theoretic quantities

Compute the distance between two fitted Bayesian networks

igraph integration

Import and export networks from the igraph package

Synthetic (discrete) data set to test learning algorithms

single-node local discovery

Discover the structure around a single node

Examination marks data set

Insurance evaluation network (synthetic) data set

Lizards' perching behaviour data set

Miscellaneous utilities

multivariate normal distribution

Gaussian Bayesian networks and multivariate normals

node ordering utilities

Partial node orderings

plot.bn.strength

Plot arc strengths derived from bootstrap

model string utilities

Build a model string from a Bayesian network and vice versa

local discovery algorithms

Local discovery structure learning algorithms

node operations

Manipulate nodes in a graph

Plot a Bayesian network

pcalg integration

Import and export networks from the pcalg package

Naive Bayes classifiers

Arc strength plot

Score of the Bayesian network

data preprocessing

Pre-process data to better learn Bayesian networks

Produce lm objects from Bayesian networks

Simulate random samples from a given Bayesian network

whitelists-blacklists

Whitelists and blacklists in structure learning

Structure learning from missing data

predict and impute

Predict or impute missing data from a Bayesian network

Manipulating the test counter

The scm class structure

structure-learning

Structure learning algorithms

Bayes factor between two network structures

ALARM monitoring system (synthetic) data set

network-classifiers

Bayesian network Classifiers

Cross-validation for Bayesian networks

Asia (synthetic) data set by Lauritzen and Spiegelhalter

utilities for whitelists and blacklists

Get or create whitelists and blacklists

Measure arc strength

Drop, add or set the direction of an arc or an edge

The bn class structure

Estimate the optimal imaginary sample size for BDe(u)