bnlearn5.0.1 package

Import and export networks from the graph package

Plotting networks with probability bars

Estimate the optimal imaginary sample size for BDe(u)

Measure arc strength

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

Bayesian network Classifiers

Bayes factor between two network structures

Get or create whitelists and blacklists

The bn class structure

Cross-validation for Bayesian networks

The bn.fit class structure

Utilities to manipulate fitted Bayesian networks

Plot fitted Bayesian networks

Fit the parameters of a Bayesian network

The bn.kcv class structure

The bn.strength class structure

Nonparametric bootstrap of Bayesian networks

Bayesian network structure learning, parameter learning and inference

Independence and conditional independence tests

Synthetic (mixed) data set to test learning algorithms

Compare two or more different Bayesian networks

Conditional independence tests

Construct configurations of discrete variables

Constraint-based structure learning algorithms

Count graphs with specific characteristics

Utilities to manipulate graphs

Equivalence classes, moral graphs and consistent extensions

Perform conditional probability queries

Test d-separation

Read and write BIF, NET, DSC and DOT files

Synthetic (continuous) data set to test learning algorithms

Import and export networks from the gRain package

Generate empty, complete or random graphs

Advanced Bayesian network plots

Score-based structure learning algorithms

Hybrid structure learning algorithms

Import and export networks from the igraph package

Compute the distance between two fitted Bayesian networks

Discover the structure around a single node

Synthetic (discrete) data set to test learning algorithms

Miscellaneous utilities

Local discovery structure learning algorithms

Build a model string from a Bayesian network and vice versa

Gaussian Bayesian networks and multivariate normals

Naive Bayes classifiers

Network scores

Manipulate nodes in a graph

Partial node orderings

Import and export networks from the pcalg package

Plot a Bayesian network

Plot arc strengths derived from bootstrap

Predict or impute missing data from a Bayesian network

Pre-process data to better learn Bayesian networks

Simulate random samples from a given Bayesian network

Score of the Bayesian network

Produce lm objects from Bayesian networks

Arc strength plot

Structure learning from missing data

Structure learning algorithms

Manipulating the test counter

Whitelists and blacklists in structure learning