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bnlearn (version 4.4.1)

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), 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 .

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Version

Install

install.packages('bnlearn')

Monthly Downloads

25,548

Version

4.4.1

License

GPL (>= 2)

Maintainer

Last Published

March 5th, 2019

Functions in bnlearn (4.4.1)

Fit the parameters of a Bayesian network

The bn.fit class structure

bn.fit utilities

Utilities to manipulate fitted Bayesian networks

Equivalence classes in the presence of interventions

Test d-separation

hybrid algorithms

Hybrid structure learning algorithms

Cross-validation for Bayesian networks

node ordering utilities

Utilities dealing with partial node orderings

Predict or impute missing data from a Bayesian network

Naive Bayes classifiers

The bn class structure

Asia (synthetic) data set by Lauritzen and Spiegelhalter

Bayes factor between two network structures

Plot a Bayesian network

The bn.kcv class structure

bn.strength class

The bn.strength class structure

Synthetic (discrete) data set to test learning algorithms

Lizards' perching behaviour data set

Insurance evaluation network (synthetic) data set

Independence and conditional independence tests

Synthetic (continuous) data set to test learning algorithms

Construct configurations of discrete variables

foreign files utilities

Read and write BIF, NET, DSC and DOT files

graph utilities

Utilities to manipulate graphs

choose.direction

Try to infer the direction of an undirected arc

Measure arc strength

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

graph enumeration

Count graphs with specific characteristics

graph generation utilities

Generate empty or random graphs

gRain integration

Import and export networks from the gRain package

Simulate random data from a given Bayesian network

Compare two or more different Bayesian networks

clgaussian.test

Synthetic (mixed) data set to test learning algorithms

score-based algorithms

Score-based structure learning algorithms

The HailFinder weather forecast system (synthetic) data set

Coronary heart disease data set

constraint-based algorithms

Constraint-based structure learning algorithms

pcalg integration

Import and export networks from the pcalg package

graph integration

Import and export networks from the graph package

Identify relevant nodes without learning the Bayesian network

local discovery algorithms

Local discovery structure learning algorithms

Arc strength plot

model string utilities

Build a model string from a Bayesian network and vice versa

single-node local discovery

Discover the structure around a single node

plot.bn.strength

Plot arc strengths derived from bootstrap

Produce lm objects from Bayesian networks

Pre-process data to better learn Bayesian networks

Structure learning from missing data

Manipulating the test counter

ALARM monitoring system (synthetic) data set

Estimate the optimal imaginary sample size for BDe(u)

Parametric and nonparametric bootstrap of Bayesian networks

bnlearn-package

Bayesian network structure learning, parameter learning and inference

Equivalence classes, moral graphs and consistent extensions

Perform conditional probability queries

Plotting networks with probability bars

Advanced Bayesian network plots

Examination marks data set

Miscellaneous utilities

ROCR integration

Generating a prediction object for ROCR

Score of the Bayesian network

Plot fitted Bayesian networks