## Usage

learn.mb(x, node, method, whitelist = NULL, blacklist = NULL, start = NULL,
test = NULL, alpha = 0.05, B = NULL, debug = FALSE)
learn.nbr(x, node, method, whitelist = NULL, blacklist = NULL,
test = NULL, alpha = 0.05, B = NULL, debug = FALSE)

## Arguments

x

a data frame containing the variables in the model.

node

a character string, the label of the node whose local structure
is being learned.

whitelist

a vector of character strings, the labels of the whitelisted
nodes.

blacklist

a vector of character strings, the labels of the blacklisted
nodes.

start

a vector of character strings, the labels of the nodes to be
included in the Markov blanket before the learning process (in
`learn.mb`

). Note that the nodes in `start`

can be removed from
the Markov blanket by the learning algorithm, unlike the nodes included due
to whitelisting.

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.

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.