rforest: Build a random forest
In henckr/distRforest: Distribution-based Random Forest

This function acts as a user-friendly interface to build a random forest based on individual rpart trees.

rforest(
  formula,
  data,
  method,
  weights = NULL,
  parms = NULL,
  control = NULL,
  ncand,
  ntrees,
  subsample = 1,
  track_oob = FALSE,
  keep_data = FALSE,
  red_mem = FALSE
)

`formula`	object of the class `formula` with a symbolic description of the form `response ~ var1 + var2 + var3` without interactions. Please refrain from applying transformation functions to the response, but add the transformed variable to the `data` beforehand. Two exceptions exist, see `method = 'poisson'` and `method = 'exp'` below.
`data`	data frame containing the training data observations.
`method`	string specifying the type of forest to build. Options are: 'class' classification forest (OOB error tracking only implemented for binary classification). 'anova' standard regression forest with a squared error loss. 'poisson' poisson regression forest for count data. The left-hand-side of `formula` can be specified as `cbind(observation_time, number_of_events)` to include time exposures. 'gamma' gamma regression forest for strictly positive long-tailed data. 'lognormal' lognormal regression forest for strictly positive long-tailed data. 'exp' exponential scaling for survival data. The left-hand-side of `formula` is specified as `Surv(observation_time, event_indicator)` to include time exposures.
`weights`	optional name of the variable in `data` to use as case weights. Either as a string or simply the variable name should work.
`parms`	optional parameters for the splitting function, see `rpart` for the details and allowed options.
`control`	list of options that control the fitting details of the `rpart` trees. Use `rpart.control` to set this up.
`ncand`	integer specifying the number of randomly chosen variable candidates to consider at each node to find the optimal split.
`ntrees`	integer specifying the number of trees in the ensemble.
`subsample`	numeric in the range [0,1]. Each tree in the ensemble is built on randomly sampled data of size `subsample * nrow(data)`.
`track_oob`	boolean to indicate whether the out-of-bag errors should be tracked (TRUE) or not (FALSE). This option is not implemented for `method = 'exp'` or multi-class classification. For the other methods, these errors are tracked: 'class' Matthews correlation coefficient for binary classification. 'anova' mean squared error. 'poisson' Poisson deviance. 'gamma' gamma deviance. 'lognormal' mean squared error. All these errors are evaluated in a weighted version if `weights` are supplied.
`keep_data`	boolean to indicate whether the `data` should be saved with the fit. Not advised to set this to `TRUE` for large data sets.
`red_mem`	boolean whether to reduce the memory footprint of the `rpart` trees by eliminating non-essential elements from the fits. It is adviced to set this to `TRUE` for large values of `ntrees`.

object of the class rforest, which is a list containing the following elements:

trees: list of length equal to ntrees, containing the individual rpart trees.
oob_error: numeric vector of length equal to ntrees, containing the OOB error at each iteration (if track_oob = TRUE).
data: the training data (if keep_data = TRUE).