gRIT: Generalized random intersection trees
In karlkumbier/iRF2.0: Iterative Random Forests

Run RIT across decision paths of a fitted random forest.

gRIT(x, y, rand.forest = NULL, read.forest = NULL,
  rit.param = list(depth = 5, ntree = 500, nchild = 2, class.id = 1,
  min.nd = 1, class.cut = NULL), varnames.grp = colnames(x),
  weights = rep(1, nrow(x)), signed = TRUE, oob.importance = TRUE,
  ints.idx.eval = NULL, ints.eval = NULL, n.core = 1)

`x`	numeric feature matrix
`y`	response vector. If factor, classification is assumed.
`rand.forest`	an object of class randomForest. Required if read.forest is NULL.
`read.forest`	output of readForest. Required if rand.forest is NULL.
`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.
`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.
`ints.idx.eval`	like `ints.eval`, but specifies the indice of the interactions instead of their names. Intended for internal use only.
`ints.eval`	interactions to evaluate. If specified, importance metrics will be evaluated for these interactions instead of those recovered by RIT.
`n.core`	number of cores to use. If -1, all available cores are used.