| alarm | ALARM monitoring system (synthetic) data set |
| alpha.star | Estimate the optimal imaginary sample size for BDe(u) |
| arcops | Drop, add or set the direction of an arc or an edge |
| arc.strength | Measure arc strength |
| asia | Asia (synthetic) data set by Lauritzen and Spiegelhalter |
| bayesian.network.classifiers | Bayesian network Classifiers |
| bf | Bayes factor between two network structures |
| blacklist | Get or create whitelists and blacklists |
| bnboot | Nonparametric bootstrap of Bayesian networks |
| bn.class | The bn class structure |
| bn.cv | Cross-validation for Bayesian networks |
| bn.fit | Fit the parameters of a Bayesian network |
| 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.kcv.class | The bn.kcv class structure |
| bnlearn-package | Bayesian network structure learning, parameter learning and... |
| bn.strength-class | The bn.strength class structure |
| 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 |
| coronary | Coronary heart disease data set |
| count.graphs | Count graphs with specific characteristics |
| 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 |
| graph | Utilities to manipulate graphs |
| graphgen | Generate empty, complete or random graphs |
| graphpkg | Import and export networks from the graph package |
| graphviz.chart | Plotting networks with probability bars |
| graphviz.plot | Advanced Bayesian network plots |
| hailfinder | The HailFinder weather forecast system (synthetic) data set |
| hc | Score-based structure learning algorithms |
| hybrid | Hybrid structure learning algorithms |
| igraphpkg | Import and export networks from the igraph package |
| insurance | Insurance evaluation network (synthetic) data set |
| 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 |
| lizards | Lizards' perching behaviour data set |
| marks | Examination marks data set |
| 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 |
| rocrpkg | Generating a prediction object for ROCR |
| 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 |
