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 |
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 |
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 |
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 |
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 |
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, complete 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 |
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 | 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 |
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 |
isolated.nodes | Miscellaneous utilities |
KL | Compute the distance between two fitted Bayesian networks |
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 |
mvnorm2gbn | Gaussian Bayesian networks and multivariate normals |
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, 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 |
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 |
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 |
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 |