iRF: Iterative random forests (iRF)
In karlkumbier/iRF2.0: Iterative Random Forests

Iteratively grow feature weighted random forests and search for prevalent interactions on decision paths.

iRF(x, y, xtest = NULL, ytest = NULL, n.iter = 5, ntree = 500,
  mtry.select.prob = rep(1, ncol(x)), iter.return = n.iter,
  int.return = NULL, select.iter = FALSE, rit.param = list(depth = 5,
  ntree = 500, nchild = 2, class.id = 1, min.nd = 1, class.cut = NULL),
  varnames.grp = colnames(x), n.bootstrap = 1, bs.sample = NULL,
  weights = rep(1, nrow(x)), signed = TRUE, oob.importance = TRUE,
  type = "randomForest", verbose = TRUE, n.core = 1,
  interactions.return = NULL, wt.pred.accuracy = NULL, ...)

`x`	numeric feature matrix.
`y`	response vector. If factor, classification is assumed.
`xtest`	numeric feature matrix for test set.
`ytest`	response vector for test set.
`n.iter`	number of iterations to run.
`ntree`	number of random forest trees.
`mtry.select.prob`	feature weights for first iteration. Defaults to equal weights
`iter.return`	which iterations should the RF be returned for. Defaults to iteration with highest OOB accuracy.
`int.return`	which iterations should interacitons be returned for.
`select.iter`	if TRUE, returns interactions from iteration with highest OOB accuracy.
`rit.param`	named list specifying RIT parameters. Entries include `depth`: depths of RITs, `ntree`: number of RITs, `nchild`: number of child nodes for each RIT, `class.id`: 0-1 indicating which leaf nodes RIT should be run over, `min.nd`: minimum node size to run RIT over, `class.cut`: threshold for converting leaf nodes in regression to binary classes.
`varnames.grp`	grouping "hyper-features" for RIT search. Features with the same name will be treated as identical for interaction search.
`n.bootstrap`	number of bootstrap samples to calculate stability scores.
`bs.sample`	list of observation indices to use for bootstrap samples. If NULL, iRF will take standard bootstrap samples of observations.
`weights`	numeric weight for each observation. Leaf nodes will be sampled for RIT with probability proprtional to the total weight of observations they contain.
`signed`	if TRUE, signed interactions will be returned.
`oob.importance`	if TRUE, importance measures are evaluated on OOB samples.
`verbose`	if TRUE, display progress of iRF fit.
`n.core`	number of cores to use. If -1, all available cores are used.
`...`	additional arguments passed to iRF::randomForest.