Nothing

```
# transform continuous data into discrete ones.
discretize = function(data, method, breaks = 3, ordered = FALSE, ..., debug = FALSE) {
# check the label of the discretization method.
method = check.discretization.method(method)
# general check on the data.
data.info = check.data(data, allow.missing = TRUE, stop.if.all.missing = TRUE)
# the data should include at least some continuous variables, otherwise we
# have nothing to do.
if (data.info$type %in% discrete.data.types) {
warning("at least one variable should be continuous")
return(data)
}#THEN
# check the number of breaks.
if (length(breaks) == 1)
breaks = rep(breaks, ncol(data))
else if (length(breaks) != ncol(data))
stop("the 'breaks' vector must have an element for each variable in the data.")
if (!is.positive.vector(breaks))
stop("the number(s) of breaks must be positive integer number(s).")
if (any(breaks == 1))
stop("the return value must have at least two levels for each variable.")
# check whether/which discretized variables should be ordered factors.
if (length(ordered) == 1)
ordered = rep(ordered, ncol(data))
else if (length(ordered) != ncol(data))
stop("the 'ordered' vector must have an element for each variable in the data.")
if (!is.logical.vector(ordered))
stop("the elements of the 'ordered' vector be logical values.")
# check the data.
if (method == "hartemink") {
# check that the data contains at least two columns, otherwise there is
# nothing to compute mutual information from.
if (ncol(data) < 2)
stop("at least two variables are needed to compute mutual information.")
}#THEN
# check debug.
check.logical(debug)
# check the extra arguments.
extra.args = check.discretization.args(method, data, breaks, list(...))
discretize.backend(data = data, method = method, breaks = breaks,
ordered = ordered, extra.args = extra.args, debug = debug)
}#DISCRETIZE
# screen the data for highly correlated variables.
dedup = function(data, threshold = 0.90, debug = FALSE) {
# check the data (only continuous data are supported).
data.info = check.data(data, allowed.types = continuous.data.types,
allow.missing = TRUE)
# check the correlation threshold.
if (missing(threshold))
threshold = 0.90
else if (!is.probability(threshold))
stop("the correlation threshold must be a number between 0 and 1.")
# check debug.
check.logical(debug)
dedup.backend(data = data, threshold = threshold,
complete = data.info$complete.nodes, debug = debug)
}#DEDUP
# configurations of sets of discrete variables.
configs = function(data, all = TRUE) {
# check the data (only discrete data are supported).
check.data(data, allowed.types = discrete.data.types)
# check the "all configurations" flag.
check.logical(all)
configurations(data, factor = TRUE, all = all)
}#CONFIGS
```

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