| alarm | ALARM Monitoring System (synthetic) data set |
| arcops | Drop, add or set the direction of an arc |
| arc.strength | Measure arc strength |
| asia | Asia (synthetic) data set by Lauritzen and Spiegelhalter |
| bnboot | Parametric and 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 |
| bn.var | Structure variability of Bayesian networks |
| choose.direction | Try to infer the direction of an undirected arc |
| ci.test | Independence and Conditional Independence Tests |
| compare | Compare two different Bayesian networks |
| constraint | Constraint-based structure learning algorithms |
| coronary | Coronary Heart Disease data set |
| cpdag | Equivalence classes, moral graphs and consistent extensions |
| cpquery | Perform conditional probability queries |
| deal | bnlearn - deal package integration |
| discretize | Discretize data to learn discrete Bayesian networks |
| dsep | Test d-separation |
| foreign | Read and write BIF, NET and DSC 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 or random graphs |
| graphpkg | Import and export networks from the graph package |
| 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 |
| insurance | Insurance evaluation network (synthetic) data set |
| 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 |
| mmpc | Local discovery structure learning algorithms |
| modelstring | Build a model string from a Bayesian network and vice versa |
| naive.bayes | Naive Bayes classifiers |
| ordering | Utilities dealing with partial node orderings |
| plot.bn | Plot a Bayesian network |
| plot.bn.strength | Plot arc strengths derived from bootstrap |
| rbn | Simulate random data from a given Bayesian network |
| relevant | Identify Relevant Nodes Without Learning the Bayesian network |
| score | Score of the Bayesian network |
| snow | bnlearn - snow/parallel package integration |
| strength.plot | Arc strength plot |
| test.counter | Manipulating the test counter |
