## Usage

hc(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ...,
debug = FALSE, restart = 0, perturb = 1, max.iter = Inf, maxp = Inf, optimized = TRUE)
tabu(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ...,
debug = FALSE, tabu = 10, max.tabu = tabu, max.iter = Inf, maxp = Inf, optimized = TRUE)

## Arguments

x

a data frame containing the variables in the model.

start

an object of class `bn`

, the preseeded directed acyclic
graph used to initialize the algorithm. If none is specified, an empty one
(i.e. without any arc) is used.

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.

score

a character string, the label of the network score to be used in
the 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. …

additional tuning parameters for the network score. See
`score`

for details. debug

a boolean value. If `TRUE`

a lot of debugging output is
printed; otherwise the function is completely silent.

restart

an integer, the number of random restarts.

tabu

a positive integer number, the length of the tabu list used in the
`tabu`

function.

max.tabu

a positive integer number, the iterations tabu search can
perform without improving the best network score.

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.

maxp

the maximum number of parents for a node. The default value is
`Inf`

.