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A B C D E F G H I L M N O P R S T U V W misc
| bnlearn-package | Bayesian network structure learning, parameter learning and inference |
| acyclic | Utilities to manipulate graphs |
| AIC.bn | Score of the Bayesian network |
| AIC.bn.fit | Utilities to manipulate fitted Bayesian networks |
| alarm | ALARM Monitoring System (synthetic) data set |
| all.equal.bn | Compare two different Bayesian networks |
| amat | Miscellaneous utilities |
| amat<- | Miscellaneous utilities |
| aracne | Local discovery structure learning algorithms |
| arc operations | Drop, add or set the direction of an arc or an edge |
| arc.strength | Measure arc strength |
| arcs | Miscellaneous utilities |
| arcs<- | Miscellaneous utilities |
| as.bn | Build a model string from a Bayesian network and vice versa |
| as.bn.character | Build a model string from a Bayesian network and vice versa |
| as.bn.fit | Import and export networks from the gRain package |
| as.bn.fit.grain | Import and export networks from the gRain package |
| as.bn.graphAM | Import and export networks from the graph package |
| as.bn.graphNEL | Import and export networks from the graph package |
| as.character.bn | Build a model string from a Bayesian network and vice versa |
| as.grain | Import and export networks from the gRain package |
| as.grain.bn.fit | Import and export networks from the gRain package |
| as.graphAM | Import and export networks from the graph package |
| as.graphAM.bn | Import and export networks from the graph package |
| as.graphAM.bn.fit | Import and export networks from the graph package |
| as.graphNEL | Import and export networks from the graph package |
| as.graphNEL.bn | Import and export networks from the graph package |
| as.graphNEL.bn.fit | Import and export networks from the graph package |
| asia | Asia (synthetic) data set by Lauritzen and Spiegelhalter |
| averaged.network | Measure arc strength |
| BIC.bn | Score of the Bayesian network |
| BIC.bn.fit | Utilities to manipulate fitted Bayesian networks |
| blacklist | Miscellaneous utilities |
| bn class | The bn class structure |
| bn-class | The bn class structure |
| bn.boot | Parametric and nonparametric bootstrap of Bayesian networks |
| 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 plots | Plot fitted Bayesian networks |
| bn.fit utilities | Utilities to manipulate fitted Bayesian networks |
| bn.fit-class | The bn.fit class structure |
| bn.fit.barchart | Plot fitted Bayesian networks |
| bn.fit.dnode | The bn.fit class structure |
| bn.fit.dotplot | Plot fitted Bayesian networks |
| bn.fit.gnode | The bn.fit class structure |
| bn.fit.histogram | Plot fitted Bayesian networks |
| bn.fit.qqplot | Plot fitted Bayesian networks |
| bn.fit.xyplot | Plot fitted Bayesian networks |
| bn.kcv class | The bn.kcv class structure |
| bn.kcv-class | The bn.kcv class structure |
| bn.kcv.list class | The bn.kcv class structure |
| bn.kcv.list-class | The bn.kcv class structure |
| bn.net | Fit the parameters of a Bayesian network |
| bn.strength | The bn.strength class structure |
| bn.strength class | The bn.strength class structure |
| bn.strength-class | The bn.strength class structure |
| bnlearn | Bayesian network structure learning, parameter learning and inference |
| boot.strength | Measure arc strength |
| cextend | Equivalence classes, moral graphs and consistent extensions |
| children | Miscellaneous utilities |
| children<- | Miscellaneous utilities |
| choose.direction | Try to infer the direction of an undirected arc |
| chow.liu | Local discovery structure learning algorithms |
| ci.test | Independence and Conditional Independence Tests |
| clgaussian.test | Synthetic (mixed) data set to test learning algorithms |
| coef.bn.fit | Utilities to manipulate fitted Bayesian networks |
| coef.bn.fit.cgnode | Utilities to manipulate fitted Bayesian networks |
| coef.bn.fit.dnode | Utilities to manipulate fitted Bayesian networks |
| coef.bn.fit.gnode | Utilities to manipulate fitted Bayesian networks |
| coef.bn.fit.onode | Utilities to manipulate fitted Bayesian networks |
| compare | Compare two different Bayesian networks |
| compelled.arcs | Miscellaneous utilities |
| configs | Construct configurations of discrete variables |
| constraint-based algorithms | Constraint-based structure learning algorithms |
| coronary | Coronary Heart Disease data set |
| cpdag | Equivalence classes, moral graphs and consistent extensions |
| cpdist | Perform conditional probability queries |
| cpquery | Perform conditional probability queries |
| custom.fit | Fit the parameters of a Bayesian network |
| custom.strength | Measure arc strength |
| deal integration | bnlearn - deal package integration |
| dedup | Pre-process data to better learn Bayesian networks |
| degree | Miscellaneous utilities |
| degree-method | Miscellaneous utilities |
| directed | Utilities to manipulate graphs |
| directed.arcs | Miscellaneous utilities |
| discretize | Pre-process data to better learn Bayesian networks |
| drop.arc | Drop, add or set the direction of an arc or an edge |
| drop.edge | Drop, add or set the direction of an arc or an edge |
| dsep | Test d-separation |
| empty.graph | Generate empty or random graphs |
| fast.iamb | Constraint-based structure learning algorithms |
| fitted.bn.fit | Utilities to manipulate fitted Bayesian networks |
| fitted.bn.fit.cgnode | Utilities to manipulate fitted Bayesian networks |
| fitted.bn.fit.dnode | Utilities to manipulate fitted Bayesian networks |
| fitted.bn.fit.gnode | Utilities to manipulate fitted Bayesian networks |
| gaussian.test | Synthetic (continuous) data set to test learning algorithms |
| gRain integration | Import and export networks from the gRain package |
| graph generation utilities | Generate empty or random graphs |
| graph integration | Import and export networks from the graph package |
| graph utilities | Utilities to manipulate graphs |
| graphviz.plot | Advanced Bayesian network plots |
| gs | Constraint-based structure learning algorithms |
| hailfinder | The HailFinder weather forecast system (synthetic) data set |
| hamming | Compare two different Bayesian networks |
| hc | Score-based structure learning algorithms |
| hybrid algorithms | Hybrid structure learning algorithms |
| iamb | Constraint-based structure learning algorithms |
| in.degree | Miscellaneous utilities |
| incident.arcs | Miscellaneous utilities |
| incoming.arcs | Miscellaneous utilities |
| increment.test.counter | Manipulating the test counter |
| insurance | Insurance evaluation network (synthetic) data set |
| inter.iamb | Constraint-based structure learning algorithms |
| leaf.nodes | Miscellaneous utilities |
| learn.mb | Discover the structure around a single node |
| learn.nbr | Discover the structure around a single node |
| learning.test | Synthetic (discrete) data set to test learning algorithms |
| lizards | Lizards' perching behaviour data set |
| local discovery algorithms | Local discovery structure learning algorithms |
| logLik.bn | Score of the Bayesian network |
| logLik.bn.fit | Utilities to manipulate fitted Bayesian networks |
| marks | Examination marks data set |
| mb | Miscellaneous utilities |
| misc utilities | Miscellaneous utilities |
| mmhc | Hybrid structure learning algorithms |
| mmpc | Local discovery structure learning algorithms |
| model string utilities | Build a model string from a Bayesian network and vice versa |
| model2network | Build a model string from a Bayesian network and vice versa |
| modelstring | Build a model string from a Bayesian network and vice versa |
| modelstring<- | Build a model string from a Bayesian network and vice versa |
| moral | Equivalence classes, moral graphs and consistent extensions |
| mutilated | Perform conditional probability queries |
| naive.bayes | Naive Bayes classifiers |
| narcs | Miscellaneous utilities |
| nbr | Miscellaneous utilities |
| nnodes | Miscellaneous utilities |
| node ordering utilities | Utilities dealing with partial node orderings |
| node.ordering | Utilities dealing with partial node orderings |
| nodes | Miscellaneous utilities |
| nodes-method | Miscellaneous utilities |
| nodes<- | Miscellaneous utilities |
| nodes<--method | Miscellaneous utilities |
| nparams | Miscellaneous utilities |
| ntests | Miscellaneous utilities |
| ordering2blacklist | Utilities dealing with partial node orderings |
| out.degree | Miscellaneous utilities |
| outgoing.arcs | Miscellaneous utilities |
| parallel integration | bnlearn - snow/parallel package integration |
| parents | Miscellaneous utilities |
| parents<- | Miscellaneous utilities |
| path | Utilities to manipulate graphs |
| pdag2dag | Utilities to manipulate graphs |
| plot.bn | Plot a Bayesian network |
| plot.bn.kcv | Cross-validation for Bayesian networks |
| plot.bn.kcv.list | Cross-validation for Bayesian networks |
| plot.bn.strength | Plot arc strengths derived from bootstrap |
| predict.bn.fit | Utilities to manipulate fitted Bayesian networks |
| predict.bn.naive | Naive Bayes classifiers |
| predict.bn.tan | Naive Bayes classifiers |
| random.graph | Generate empty or random graphs |
| rbn | Simulate random data from a given Bayesian network |
| rbn.bn | Simulate random data from a given Bayesian network |
| rbn.bn.fit | Simulate random data from a given Bayesian network |
| read.bif | Read and write BIF, NET, DSC and DOT files |
| read.dsc | Read and write BIF, NET, DSC and DOT files |
| read.net | Read and write BIF, NET, DSC and DOT files |
| relevant | Identify Relevant Nodes Without Learning the Bayesian network |
| reset.test.counter | Manipulating the test counter |
| residuals.bn.fit | Utilities to manipulate fitted Bayesian networks |
| residuals.bn.fit.cgnode | Utilities to manipulate fitted Bayesian networks |
| residuals.bn.fit.dnode | Utilities to manipulate fitted Bayesian networks |
| residuals.bn.fit.gnode | Utilities to manipulate fitted Bayesian networks |
| reverse.arc | Drop, add or set the direction of an arc or an edge |
| reversible.arcs | Miscellaneous utilities |
| root.nodes | Miscellaneous utilities |
| rsmax2 | Hybrid structure learning algorithms |
| score | Score of the Bayesian network |
| score-based algorithms | Score-based structure learning algorithms |
| set.arc | Drop, add or set the direction of an arc or an edge |
| set.edge | Drop, add or set the direction of an arc or an edge |
| shd | Compare two different Bayesian networks |
| si.hiton.pc | Local discovery structure learning algorithms |
| sigma | Utilities to manipulate fitted Bayesian networks |
| sigma.bn.fit | Utilities to manipulate fitted Bayesian networks |
| sigma.bn.fit.cgnode | Utilities to manipulate fitted Bayesian networks |
| sigma.bn.fit.gnode | Utilities to manipulate fitted Bayesian networks |
| single-node local discovery | Discover the structure around a single node |
| skeleton | Utilities to manipulate graphs |
| strength.plot | Arc strength plot |
| subgraph | Utilities to manipulate graphs |
| tabu | Score-based structure learning algorithms |
| test.counter | Manipulating the test counter |
| tiers2blacklist | Utilities dealing with partial node orderings |
| tree.bayes | Naive Bayes classifiers |
| undirected.arcs | Miscellaneous utilities |
| vstructs | Equivalence classes, moral graphs and consistent extensions |
| whitelist | Miscellaneous utilities |
| write.bif | Read and write BIF, NET, DSC and DOT files |
| write.dot | Read and write BIF, NET, DSC and DOT files |
| write.dsc | Read and write BIF, NET, DSC and DOT files |
| write.net | Read and write BIF, NET, DSC and DOT files |
| $<-.bn.fit | Fit the parameters of a Bayesian network |