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bnlearn (version 4.7.1)

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, 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 .

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Version

Install

install.packages('bnlearn')

Monthly Downloads

25,548

Version

4.7.1

License

GPL (>= 2)

Maintainer

Last Published

March 31st, 2022

Functions in bnlearn (4.7.1)

Cross-validation for Bayesian networks

ALARM monitoring system (synthetic) data set

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

Asia (synthetic) data set by Lauritzen and Spiegelhalter

network-classifiers

Bayesian network Classifiers

Measure arc strength

Estimate the optimal imaginary sample size for BDe(u)

The bn class structure

utilities for whitelists and blacklists

Get or create whitelists and blacklists

Bayes factor between two network structures

bn.fit utilities

Utilities to manipulate fitted Bayesian networks

choose.direction

Try to infer the direction of an undirected arc

bnlearn-package

Bayesian network structure learning, parameter learning and inference

Nonparametric bootstrap of Bayesian networks

The bn.kcv class structure

bn.strength class

The bn.strength class structure

clgaussian.test

Synthetic (mixed) data set to test learning algorithms

Independence and conditional independence tests

Compare two or more different Bayesian networks

Test d-separation

Perform conditional probability queries

score-based algorithms

Score-based structure learning algorithms

Coronary heart disease data set

constraint-based algorithms

Constraint-based structure learning algorithms

Plotting networks with probability bars

The HailFinder weather forecast system (synthetic) data set

Plot fitted Bayesian networks

Advanced Bayesian network plots

independence-tests

Conditional independence tests

single-node local discovery

Discover the structure around a single node

Synthetic (continuous) data set to test learning algorithms

Synthetic (discrete) data set to test learning algorithms

Examination marks data set

Lizards' perching behaviour data set

Plot a Bayesian network

plot.bn.strength

Plot arc strengths derived from bootstrap

data preprocessing

Pre-process data to better learn Bayesian networks

Simulate random samples from a given Bayesian network

graph utilities

Utilities to manipulate graphs

node ordering utilities

Partial node orderings

local discovery algorithms

Local discovery structure learning algorithms

Miscellaneous utilities

pcalg integration

Import and export networks from the pcalg package

Predict or impute missing data from a Bayesian network

Insurance evaluation network (synthetic) data set

ROCR integration

Generating a prediction object for ROCR

Fit the parameters of a Bayesian network

Construct configurations of discrete variables

node operations

Manipulate nodes in a graph

graph generation utilities

Generate empty or random graphs

Score of the Bayesian network

The bn.fit class structure

Manipulating the test counter

graph integration

Import and export networks from the graph package

whitelists-blacklists

Whitelists and blacklists in structure learning

foreign files utilities

Read and write BIF, NET, DSC and DOT files

graph enumeration

Count graphs with specific characteristics

Equivalence classes, moral graphs and consistent extensions

hybrid algorithms

Hybrid structure learning algorithms

model string utilities

Build a model string from a Bayesian network and vice versa

gRain integration

Import and export networks from the gRain package

Naive Bayes classifiers

igraph integration

Import and export networks from the igraph package

Produce lm objects from Bayesian networks

Structure learning from missing data

Arc strength plot

structure-learning

Structure learning algorithms