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bnlearn (version 4.9.4)

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

4.9.4

License

GPL (>= 2)

Maintainer

Last Published

May 2nd, 2024

Functions in bnlearn (4.9.4)

bn.fit utilities

Utilities to manipulate fitted Bayesian networks

bn.strength class

The bn.strength class structure

Nonparametric bootstrap of Bayesian networks

clgaussian.test

Synthetic (mixed) data set to test learning algorithms

Independence and conditional independence tests

bnlearn-package

Bayesian network structure learning, parameter learning and inference

The bn.kcv class structure

Fit the parameters of a Bayesian network

The bn.fit class structure

Plot fitted Bayesian networks

Compare two or more different Bayesian networks

Coronary heart disease data set

foreign files utilities

Read and write BIF, NET, DSC and DOT files

independence-tests

Conditional independence tests

graph enumeration

Count graphs with specific characteristics

Test d-separation

Construct configurations of discrete variables

Perform conditional probability queries

Equivalence classes, moral graphs and consistent extensions

constraint-based algorithms

Constraint-based structure learning algorithms

graph utilities

Utilities to manipulate graphs

graph integration

Import and export networks from the graph package

gRain integration

Import and export networks from the gRain package

Synthetic (continuous) data set to test learning algorithms

score-based algorithms

Score-based structure learning algorithms

Examination marks data set

igraph integration

Import and export networks from the igraph package

Lizards' perching behaviour data set

model string utilities

Build a model string from a Bayesian network and vice versa

local discovery algorithms

Local discovery structure learning algorithms

single-node local discovery

Discover the structure around a single node

node operations

Manipulate nodes in a graph

The HailFinder weather forecast system (synthetic) data set

Compute the distance between two fitted Bayesian networks

Plotting networks with probability bars

Advanced Bayesian network plots

hybrid algorithms

Hybrid structure learning algorithms

Synthetic (discrete) data set to test learning algorithms

Insurance evaluation network (synthetic) data set

graph generation utilities

Generate empty, complete or random graphs

Miscellaneous utilities

predict and impute

Predict or impute missing data from a Bayesian network

data preprocessing

Pre-process data to better learn Bayesian networks

Plot a Bayesian network

plot.bn.strength

Plot arc strengths derived from bootstrap

whitelists-blacklists

Whitelists and blacklists in structure learning

multivariate normal distribution

Gaussian Bayesian networks and multivariate normals

Manipulating the test counter

structure-learning

Structure learning algorithms

Arc strength plot

node ordering utilities

Partial node orderings

pcalg integration

Import and export networks from the pcalg package

Simulate random samples from a given Bayesian network

ROCR integration

Generating a prediction object for ROCR

Naive Bayes classifiers

Produce lm objects from Bayesian networks

Score of the Bayesian network

Structure learning from missing data

Asia (synthetic) data set by Lauritzen and Spiegelhalter

ALARM monitoring system (synthetic) data set

network-classifiers

Bayesian network Classifiers

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

The bn class structure

Cross-validation for Bayesian networks

Estimate the optimal imaginary sample size for BDe(u)

Measure arc strength

utilities for whitelists and blacklists

Get or create whitelists and blacklists

Bayes factor between two network structures