## Usage

gs(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE,
undirected = FALSE)
iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE,
undirected = FALSE)
fast.iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE,
undirected = FALSE)
inter.iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE,
undirected = FALSE)
mmpc(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE,
undirected = TRUE)
si.hiton.pc(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE,
undirected = TRUE)

## Arguments

x

a data frame containing the variables in the model.

cluster

an optional cluster object from package parallel. See
`parallel integration`

for details and a simple example. whitelist

a data frame with two columns (optionally labeled "from"
and "to"), containing a set of arcs to be included in the graph.

blacklist

a data frame with two columns (optionally labeled "from"
and "to"), containing a set of arcs not to be included in the graph.

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.

strict

a boolean value. If `TRUE`

conflicting results in the
learning process generate an error; otherwise they result in a warning.

undirected

a boolean value. If `TRUE`

no attempt will be made to
determine the orientation of the arcs; the returned (undirected) graph
will represent the underlying structure of the Bayesian network.