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bnlearn (version 4.5)

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

20,455

Version

4.5

License

GPL (>= 2)

Maintainer

Marco Scutari

Last Published

August 5th, 2019

Functions in bnlearn (4.5)

network-classifiers

Bayesian network Classifiers

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

The bn class structure

ALARM monitoring system (synthetic) data set

Bayes factor between two network structures

Asia (synthetic) data set by Lauritzen and Spiegelhalter

Cross-validation for Bayesian networks

Estimate the optimal imaginary sample size for BDe(u)

Measure arc strength

bn.strength class

The bn.strength class structure

Independence and conditional independence tests

bnlearn-package

Bayesian network structure learning, parameter learning and inference

Plot fitted Bayesian networks

Parametric and nonparametric bootstrap of Bayesian networks

Compare two or more different Bayesian networks

graph enumeration

Count graphs with specific characteristics

graph integration

Import and export networks from the graph package

Fit the parameters of a Bayesian network

graph generation utilities

Generate empty or random graphs

Coronary heart disease data set

local discovery algorithms

Local discovery structure learning algorithms

independence-tests

Conditional independence tests

choose.direction

Try to infer the direction of an undirected arc

model string utilities

Build a model string from a Bayesian network and vice versa

The bn.fit class structure

clgaussian.test

Synthetic (mixed) data set to test learning algorithms

Miscellaneous utilities

score-based algorithms

Score-based structure learning algorithms

Pre-process data to better learn Bayesian networks

Examination marks data set

Simulate random samples from a given Bayesian network

The HailFinder weather forecast system (synthetic) data set

bn.fit utilities

Utilities to manipulate fitted Bayesian networks

Structure learning from missing data

Produce lm objects from Bayesian networks

Equivalence classes, moral graphs and consistent extensions

hybrid algorithms

Hybrid structure learning algorithms

Arc strength plot

Synthetic (continuous) data set to test learning algorithms

constraint-based algorithms

Constraint-based structure learning algorithms

The bn.kcv class structure

Construct configurations of discrete variables

structure-learning

Structure learning algorithms

graph utilities

Utilities to manipulate graphs

Synthetic (discrete) data set to test learning algorithms

Perform conditional probability queries

Lizards' perching behaviour data set

single-node local discovery

Discover the structure around a single node

Insurance evaluation network (synthetic) data set

Advanced Bayesian network plots

Plotting networks with probability bars

Equivalence classes in the presence of interventions

Test d-separation

Plot a Bayesian network

Naive Bayes classifiers

Predict or impute missing data from a Bayesian network

plot.bn.strength

Plot arc strengths derived from bootstrap

node ordering utilities

Utilities dealing with partial node orderings

gRain integration

Import and export networks from the gRain package

foreign files utilities

Read and write BIF, NET, DSC and DOT files

ROCR integration

Generating a prediction object for ROCR

Score of the Bayesian network

pcalg integration

Import and export networks from the pcalg package

Manipulating the test counter

whitelists-blacklists

Whitelists and blacklists in structure learning