bnlearn5.0.1 package

Bayesian Network Structure Learning, Parameter Learning and Inference

graphpkg

Import and export networks from the graph package

graphviz.chart

Plotting networks with probability bars

alpha.star

Estimate the optimal imaginary sample size for BDe(u)

arc.strength

Measure arc strength

arcops

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

bayesian.network.classifiers

Bayesian network Classifiers

bf

Bayes factor between two network structures

blacklist

Get or create whitelists and blacklists

bn.class

The bn class structure

bn.cv

Cross-validation for Bayesian networks

bn.fit.class

The bn.fit class structure

bn.fit.methods

Utilities to manipulate fitted Bayesian networks

bn.fit.plots

Plot fitted Bayesian networks

bn.fit

Fit the parameters of a Bayesian network

bn.kcv.class

The bn.kcv class structure

bn.strength-class

The bn.strength class structure

bnboot

Nonparametric bootstrap of Bayesian networks

bnlearn-package

Bayesian network structure learning, parameter learning and inference

ci.test

Independence and conditional independence tests

clgaussian-test

Synthetic (mixed) data set to test learning algorithms

compare

Compare two or more different Bayesian networks

conditional.independence.tests

Conditional independence tests

configs

Construct configurations of discrete variables

constraint

Constraint-based structure learning algorithms

count.graphs

Count graphs with specific characteristics

graph

Utilities to manipulate graphs

cpdag

Equivalence classes, moral graphs and consistent extensions

cpquery

Perform conditional probability queries

dsep

Test d-separation

foreign

Read and write BIF, NET, DSC and DOT files

gaussian-test

Synthetic (continuous) data set to test learning algorithms

gRain

Import and export networks from the gRain package

graphgen

Generate empty, complete or random graphs

graphviz.plot

Advanced Bayesian network plots

hc

Score-based structure learning algorithms

hybrid

Hybrid structure learning algorithms

igraphpkg

Import and export networks from the igraph package

kl

Compute the distance between two fitted Bayesian networks

learn

Discover the structure around a single node

learning-test

Synthetic (discrete) data set to test learning algorithms

mb

Miscellaneous utilities

mi.matrix

Local discovery structure learning algorithms

modelstring

Build a model string from a Bayesian network and vice versa

mvnorm

Gaussian Bayesian networks and multivariate normals

naive.bayes

Naive Bayes classifiers

network.scores

Network scores

nodeops

Manipulate nodes in a graph

ordering

Partial node orderings

pcalg

Import and export networks from the pcalg package

plot.bn

Plot a Bayesian network

plot.bn.strength

Plot arc strengths derived from bootstrap

predict.and.impute

Predict or impute missing data from a Bayesian network

preprocessing

Pre-process data to better learn Bayesian networks

rbn

Simulate random samples from a given Bayesian network

score

Score of the Bayesian network

statspkg

Produce lm objects from Bayesian networks

strength.plot

Arc strength plot

structural.em

Structure learning from missing data

structure.learning

Structure learning algorithms

test.counter

Manipulating the test counter

whitelists.and.blacklists

Whitelists and blacklists in structure learning

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 <https://www.bnlearn.com/>.