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bnlearn (version 5.2)

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

License

GPL (>= 2)

Maintainer

Marco Scutari

Last Published

July 13th, 2026

Functions in bnlearn (5.2)

Nonparametric bootstrap of Bayesian networks

bnlearn-package

Bayesian network structure learning, parameter learning and inference

Plot fitted Bayesian networks

Coronary heart disease data set

bn.strength class

The bn.strength class structure

graph enumeration

Count graphs with specific characteristics

The bn.fit class structure

Perform conditional probability queries

causal discovery algorithms

Causal discovery algorithms

bn.fit utilities

Utilities to manipulate fitted Bayesian networks

clgaussian.test

Synthetic (mixed) data set to test learning algorithms

Independence and conditional independence tests

independence-tests

Conditional independence tests

The bn.kcv class structure

Construct configurations of discrete variables

causal inference

Perform causal inference

Equivalence classes, moral graphs and consistent extensions

local discovery algorithms

Local discovery structure learning algorithms

single-node local discovery

Discover the structure around a single node

Miscellaneous utilities

Examination marks data set

Plot a Bayesian network

node ordering utilities

Partial node orderings

Plotting networks with parameter summaries

node operations

Manipulate nodes in a graph

hybrid algorithms

Hybrid structure learning algorithms

gRain integration

Import and export networks from the gRain package

graph utilities

Utilities to manipulate graphs

foreign files utilities

Read and write BIF, NET, DSC and DOT files

Synthetic (discrete) data set to test learning algorithms

Lizards' perching behaviour data set

Test d-separation

Insurance evaluation network (synthetic) data set

score-based algorithms

Score-based structure learning algorithms

The scm class structure

constraint-based algorithms

Constraint-based structure learning algorithms

The HailFinder weather forecast system (synthetic) data set

Structure learning from missing data

graph integration

Import and export networks from the graph package

graph generation utilities

Generate empty, complete or random graphs

Fit the parameters of a Bayesian network

multivariate normal distribution

Gaussian Bayesian networks and multivariate normals

Synthetic (continuous) data set to test learning algorithms

model string utilities

Build a model string from a Bayesian network and vice versa

Arc strength plot

Simulate random samples from a given Bayesian network

pcalg integration

Import and export networks from the pcalg package

igraph integration

Import and export networks from the igraph package

Compare two or more different Bayesian networks

information theoretic quantities

Compute the distance between two fitted Bayesian networks

Advanced Bayesian network plots

Manipulating the test counter

Produce lm objects from Bayesian networks

data preprocessing

Pre-process data to better learn Bayesian networks

plot.bn.strength

Plot arc strengths derived from bootstrap

whitelists-blacklists

Whitelists and blacklists in structure learning

structure-learning

Structure learning algorithms

Score of the Bayesian network

Naive Bayes classifiers

predict and impute

Predict or impute missing data from a Bayesian network

ALARM monitoring system (synthetic) data set

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

The bn class structure

Asia (synthetic) data set by Lauritzen and Spiegelhalter

utilities for whitelists and blacklists

Get or create whitelists and blacklists

Bayes factor between two network structures

network-classifiers

Bayesian network Classifiers

Measure arc strength

Estimate the optimal imaginary sample size for BDe(u)

Cross-validation for Bayesian networks