lcd-package | Structural learning of chain graphs via the decomposition approach |
alarm.net | Graph structure of ALARM network |
all.equal-method | All.equal method for sep.pair class |
all.equal-methods | All.equal method for sep.pair class |
as.freq.tb | Frequency table transformation |
comp.pat | Pattern comparison |
comp.skel | Skeleton comparison |
compress.freq.tb | Frequency table compression |
draw | Draw graph |
freq.tb-class | Class "freq.tb" |
get.multinom.dist | Random multinomial distribution generation from a chain graph |
get.normal.dist | Random normal distribution generation from a chain graph |
is.chaingraph | Chain graph verification |
is.separated | c-separation on the chain graph |
lcd | Structural learning of chain graphs via the decomposition approach |
learn.complex.multinom | Graph learning functions |
learn.complex.norm | Graph learning functions |
learn.mec.multinom | Graph learning functions |
learn.mec.norm | Graph learning functions |
learn.skeleton.multinom | Graph learning functions |
learn.skeleton.norm | Graph learning functions |
learn.v | Graph learning functions |
maxcard.search | Maximum cardinality search |
moralize | Chain graph moralization |
multinom.ci.test | Conditional independence test for multinomial data |
naive.getug.multinom | A naive function to get an undirected graph for multinomial data |
naive.getug.norm | A naive function to get an undirected graph for normal data |
norm.ci.test | Conditional independence test for multivariate normal data |
pattern | Chain graph pattern computation |
rmultinom.cg | Random multinomial sample from a chain graph |
rnorm.cg | Random normal sample from a chain graph |
sep.pair-class | Class "sep.pair" |
sep.tree-class | Class "sep.tree" |
show-method | Show method for sep.pair class |
show-methods | Show method for sep.pair class |
skeleton | Graph skeleton |
toy.graph | Graph structure of a toy example |
tri.ug | Triangulation of an undirected graph |
ug.to.jtree | Junction tree construction for undirected graph |