Function index (in alphabetical order)

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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
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 probability bars
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
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 --

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