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 |

add.node | Manipulate nodes in a graph |

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 or more different Bayesian networks |

alpha.star | Estimate the optimal imaginary sample size for BDe(u) |

amat | Miscellaneous utilities |

amat<- | Miscellaneous utilities |

ancestors | 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.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.bn.igraph | Import and export networks from the igraph package |

as.bn.pcAlgo | Import and export networks from the pcalg 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 | 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 |

as.igraph | Import and export networks from the igraph package |

as.igraph.bn | Import and export networks from the igraph package |

as.igraph.bn.fit | Import and export networks from the igraph package |

as.lm | Produce lm objects from Bayesian networks |

as.lm.bn | Produce lm objects from Bayesian networks |

as.lm.bn.fit | Produce lm objects from Bayesian networks |

as.lm.bn.fit.gnode | Produce lm objects from Bayesian networks |

as.prediction | Generating a prediction object for ROCR |

as.prediction.bn.strength | Generating a prediction object for ROCR |

asia | Asia (synthetic) data set by Lauritzen and Spiegelhalter |

averaged.network | Measure arc strength |

BF | Bayes factor between two network structures |

bf.strength | Measure arc strength |

BIC.bn | Score of the Bayesian network |

BIC.bn.fit | Utilities to manipulate fitted Bayesian networks |

blacklist | Get or create whitelists and blacklists |

bn class | The bn class structure |

bn-class | The bn class structure |

bn.boot | 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 |

colliders | Equivalence classes, moral graphs and consistent extensions |

compare | Compare two or more 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 |

count.graphs | Count graphs with specific characteristics |

cpdag | Equivalence classes, moral graphs and consistent extensions |

cpdist | Perform conditional probability queries |

cpquery | Perform conditional probability queries |

ctsdag | Equivalence classes in the presence of interventions |

custom.fit | Fit the parameters of a Bayesian network |

custom.strength | Measure arc strength |

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

degree | Miscellaneous utilities |

degree-method | Miscellaneous utilities |

descendants | 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 |

em-based algorithms | Structure learning from missing data |

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 enumeration | Count graphs with specific characteristics |

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.chart | Plotting networks with probability bars |

graphviz.compare | Compare two or more different Bayesian networks |

graphviz.plot | Advanced Bayesian network plots |

gs | Constraint-based structure learning algorithms |

h2pc | Hybrid structure learning algorithms |

hailfinder | The HailFinder weather forecast system (synthetic) data set |

hamming | Compare two or more different Bayesian networks |

hc | Score-based structure learning algorithms |

hpc | Constraint-based structure learning algorithms |

hybrid algorithms | Hybrid structure learning algorithms |

iamb | Constraint-based structure learning algorithms |

iamb.fdr | Constraint-based structure learning algorithms |

igraph integration | Import and export networks from the igraph package |

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

in.degree | Miscellaneous utilities |

incident.arcs | Miscellaneous utilities |

incoming.arcs | Miscellaneous utilities |

increment.test.counter | Manipulating the test counter |

independence tests | Conditional independence tests |

independence-tests | Conditional independence tests |

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 |

lm integration | Produce lm objects from Bayesian networks |

local discovery algorithms | Local discovery structure learning algorithms |

logLik.bn | Score of the Bayesian network |

logLik.bn.fit | Utilities to manipulate fitted Bayesian networks |

loss | Cross-validation for Bayesian networks |

marks | Examination marks data set |

mb | Miscellaneous utilities |

mean.bn.strength | Measure arc strength |

misc utilities | Miscellaneous utilities |

mmhc | Hybrid structure learning algorithms |

mmpc | Constraint-based 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 |

network classifiers | Bayesian network Classifiers |

network scores | Network scores |

network-classifiers | Bayesian network Classifiers |

network-scores | Network scores |

nnodes | Miscellaneous utilities |

node operations | Manipulate nodes in a graph |

node ordering utilities | Partial node orderings |

node.ordering | Partial node orderings |

nodes | Miscellaneous utilities |

nodes-method | Miscellaneous utilities |

nodes<- | Manipulate nodes in a graph |

nodes<--method | Manipulate nodes in a graph |

nparams | Miscellaneous utilities |

ntests | Miscellaneous utilities |

ordering2blacklist | Get or create whitelists and blacklists |

out.degree | Miscellaneous utilities |

outgoing.arcs | Miscellaneous utilities |

parents | Miscellaneous utilities |

parents<- | Miscellaneous utilities |

path | Utilities to manipulate graphs |

path-method | Utilities to manipulate graphs |

path.exists | Utilities to manipulate graphs |

pc.stable | Constraint-based structure learning algorithms |

pcalg integration | Import and export networks from the pcalg package |

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 | Predict or impute missing data from a Bayesian network |

predict.bn.naive | Naive Bayes classifiers |

predict.bn.tan | Naive Bayes classifiers |

random.graph | Generate empty or random graphs |

rbn | Simulate random samples from a given Bayesian network |

rbn.bn | Simulate random samples from a given Bayesian network |

rbn.bn.fit | Simulate random samples 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 |

remove.node | Manipulate nodes in a graph |

rename.nodes | Manipulate nodes in a graph |

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 |

ROCR integration | Generating a prediction object for ROCR |

root.nodes | Miscellaneous utilities |

rsmax2 | Hybrid structure learning algorithms |

score | Score of the Bayesian network |

score-based algorithms | Score-based structure learning algorithms |

score-method | Score of the Bayesian network |

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 |

set2blacklist | Get or create whitelists and blacklists |

shd | Compare two or more different Bayesian networks |

shielded.colliders | Equivalence classes, moral graphs and consistent extensions |

si.hiton.pc | Constraint-based 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 |

spouses | Miscellaneous utilities |

strength.plot | Arc strength plot |

structural.em | Structure learning from missing data |

structure learning | Structure learning algorithms |

structure-learning | Structure learning algorithms |

subgraph | Utilities to manipulate graphs |

tabu | Score-based structure learning algorithms |

test.counter | Manipulating the test counter |

tiers2blacklist | Get or create whitelists and blacklists |

tree.bayes | Naive Bayes classifiers |

undirected.arcs | Miscellaneous utilities |

unshielded.colliders | Equivalence classes, moral graphs and consistent extensions |

vstructs | Equivalence classes, moral graphs and consistent extensions |

whitelist | Get or create whitelists and blacklists |

whitelists and blacklists | Whitelists and blacklists in structure learning |

whitelists-blacklists | Whitelists and blacklists in structure learning |

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 |