# Bayesian Network Structure Learning, Parameter Learning and Inference

## Documentation for package ‘bnlearn’ version 5.2.1

- DESCRIPTION file

## Help Pages

A B C D E F G H I K L M N O P R S T U V W misc

bnlearn-package | Bayesian network structure learning, parameter learning and inference

## -- A --

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)
alst | Miscellaneous utilities
alst<- | Miscellaneous utilities
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.scm | The scm class structure
as.scm.bn | The scm class structure
asia | Asia (synthetic) data set by Lauritzen and Spiegelhalter
averaged.network | Measure arc strength

## -- B --

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

## -- C --

causal discovery algorithms | Causal discovery algorithms
cextend | Equivalence classes, moral graphs and consistent extensions
cextend.all | Equivalence classes, moral graphs and consistent extensions
children | Miscellaneous utilities
children<- | Miscellaneous utilities
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
complete.graph | Generate empty, complete or random graphs
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
counterfactual | Perform causal inference
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

## -- D --

dedup | Pre-process data to better learn Bayesian networks
degree | Miscellaneous utilities
degree-method | Miscellaneous utilities
descendants | Miscellaneous utilities
direct.lingam | Causal discovery algorithms
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

## -- E --

em-based algorithms | Structure learning from missing data
empty.graph | Generate empty, complete or random graphs

## -- F --

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

## -- G --

gaussian.test | Synthetic (continuous) data set to test learning algorithms
gbn2mvnorm | Gaussian Bayesian networks and multivariate normals
gRain integration | Import and export networks from the gRain package
graph enumeration | Count graphs with specific characteristics
graph generation utilities | Generate empty, complete or random graphs
graph integration | Import and export networks from the graph package
graph utilities | Utilities to manipulate graphs
graphviz.chart | Plotting networks with parameter summaries
graphviz.compare | Compare two or more different Bayesian networks
graphviz.plot | Advanced Bayesian network plots
gs | Constraint-based structure learning algorithms

## -- H --

H | Compute the distance between two fitted Bayesian networks
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

## -- I --

iamb | Constraint-based structure learning algorithms
iamb.fdr | Constraint-based structure learning algorithms
identifiable | Utilities to manipulate fitted Bayesian networks
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
inclusion.threshold | Measure arc strength
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
intervention | Perform causal inference
isolated.nodes | Miscellaneous utilities

## -- K --

KL | Compute the distance between two fitted Bayesian networks

## -- L --

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

## -- M --

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 causal inference
mvnorm2gbn | Gaussian Bayesian networks and multivariate normals

## -- N --

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

## -- O --

ordering2blacklist | Get or create whitelists and blacklists
out.degree | Miscellaneous utilities
outgoing.arcs | Miscellaneous utilities

## -- P --

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
perturb | 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

## -- R --

random.graph | Generate empty, complete or random graphs
rbn | 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
root.nodes | Miscellaneous utilities
rsmax2 | Hybrid structure learning algorithms

## -- S --

scm class | The scm class structure
scm-class | The scm class structure
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
sid | Compare two or more different Bayesian networks
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
singular | Utilities to manipulate fitted Bayesian networks
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

## -- T --

tabu | Score-based structure learning algorithms
test.counter | Manipulating the test counter
tiers2blacklist | Get or create whitelists and blacklists
tree.bayes | Naive Bayes classifiers
twin | Perform causal inference

## -- U --

undirected.arcs | Miscellaneous utilities
unshielded.colliders | Equivalence classes, moral graphs and consistent extensions

## -- V --

valid.cpdag | Utilities to manipulate graphs
valid.dag | Utilities to manipulate graphs
valid.ug | Utilities to manipulate graphs
vstructs | Equivalence classes, moral graphs and consistent extensions

## -- W --

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

## -- misc --

$<-.bn.fit | Fit the parameters of a Bayesian network