Bayesian Network Structure Learning, Parameter Learning and Inference
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
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/>.