RandomForest: Fit a random forest model
In 19900321/flipMultivariates: Multivariate models

Fit a random forest model

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RandomForest(formula, data = NULL, subset = NULL, weights = NULL,
  output = "Importance", missing = "Exclude cases with missing data",
  seed = 12321, show.labels = FALSE, sort.by.importance = TRUE, ...)

`formula`	A formula of the form `groups ~ x1 + x2 + ...` That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators, and any transformations, interactions, or other non-additive operators apart from `.` will be ignored.
`data`	A `data.frame` from which variables specified in formula are preferentially to be taken.
`subset`	An optional vector specifying a subset of observations to be used in the fitting process, or the name of a variable in `data`. It may not be an expression.
`weights`	An optional vector of sampling weights, or the name of a variable in `data`. It may not be an expression.
`output`	One of `"Importance"`, `"Prediction-Accuracy Table"` or `"Detail"`.
`missing`	How missing data is to be treated. Options: `"Error if missing data"`, `"Exclude cases with missing data"`, or `"Imputation (replace missing values with estimates)"`.
`seed`	The random number seed.
`show.labels`	Shows the variable labels, as opposed to the labels, in the outputs, where a variables label is an attribute (e.g., attr(foo, "label")).
`sort.by.importance`	Sort the last column of the importance table in descending order.
`...`	Other arguments to be supplied to `randomForest`.