a vector of character strings, the labels of nodes whose
conditional probability distributions are of interest.

context

a vector of character strings, the labels of nodes on
which to condition the independence tests.

data

a data frame containing either numeric or factor columns.

test

a character string, the label of the conditional independence
test to be used in the algorithm. If none is specified, the default test
statistic is the mutual information for categorical variables, the
Jonckheere-Terpstra test for ordered factors and the linear
correlation for continuous variables. See bnlearn-package
for details.

alpha

a numeric value, the target nominal type I error rate. If none
is specified, the default value is 0.05.

B

a positive integer, the number of permutations considered for each
permutation test. It will be ignored with a warning if the conditional
independence test specified by the test argument is not a
permutation test.

debug

a boolean value. If TRUE a lot of debugging output is
printed; otherwise the function is completely silent.

Value

relevant returns a vector of character strings, the labels of the
relevant nodes.

References

Pena JM, Nilsson R, Bjorkegren J, Tegner J (2006). "Identifying the Relevant
Nodes Without Learning the Model". In "Proceedings of the 22nd Conference
on Uncertainty in Artificial Intelligence (UAI2006)", pp. 367-374.