## Usage

rsmax2(x, whitelist = NULL, blacklist = NULL, restrict, maximize = "hc",
test = NULL, score = NULL, alpha = 0.05, B = NULL, ...,
maximize.args = list(), optimized = TRUE, strict = FALSE, debug = FALSE)
mmhc(x, whitelist = NULL, blacklist = NULL, test = NULL, score = NULL,
alpha = 0.05, B = NULL, ..., restart = 0, perturb = 1, max.iter = Inf,
optimized = TRUE, strict = FALSE, debug = FALSE)

## Arguments

x

a data frame containing the variables in the model.

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.

restrict

a character string, the constraint-based algorithm to be used
in the “restrict” phase. Possible values are `gs`

, `iamb`

,
`fast.iamb`

, `inter.iamb`

and `mmpc`

. See
`bnlearn-package`

and the documentation of each algorithm for
details. maximize

a character string, the score-based algorithm to be used in
the “maximize” phase. Possible values are `hc`

and `tabu`

.
See `bnlearn-package`

for details. test

a character string, the label of the conditional independence test
to be used by the constraint-based 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. score

a character string, the label of the network score to be used in
the score-based algorithm. If none is specified, the default score is the
*Bayesian Information Criterion* for both discrete and continuous data
sets. See `bnlearn-package`

for details. alpha

a numeric value, the target nominal type I error rate of the
conditional independence test.

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.

…

additional tuning parameters for the network score used by the
score-based algorithm. See `score`

for details. maximize.args

a list of arguments to be passed to the score-based
algorithm specified by `maximize`

, such as `restart`

for
hill-climbing or `tabu`

for tabu search.

restart

an integer, the number of random restarts for the score-based
algorithm.

perturb

an integer, the number of attempts to randomly
insert/remove/reverse an arc on every random restart.

max.iter

an integer, the maximum number of iterations for the
score-based algorithm.

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.