Bayesian Network Structure Learning, Parameter Learning and Inference

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 |

bf | Bayes factor between two network structures |

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 |

choose.direction | Try to infer the direction of an undirected arc |

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 |

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 |

ctsdag | Equivalence classes in the presence of interventions |

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 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 |

impute | Predict or impute missing data from a Bayesian network |

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 |

mi.matrix | 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 |

pcalg | Import and export networks from the pcalg package |

plot.bn | Plot a Bayesian network |

plot.bn.strength | Plot arc strengths derived from bootstrap |

preprocessing | Pre-process data to better learn Bayesian networks |

rbn | Simulate random data from a given Bayesian network |

relevant | Identify relevant nodes without learning the 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 |

test.counter | Manipulating the test counter |

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