stabilityScore: Stability score
In karlkumbier/iRF2.0: Iterative Random Forests

Run outer layer bootstrap stability analysis

stabilityScore(x, y, ntree = 500, mtry.select.prob = rep(1, ncol(x)),
  ints.idx.eval = NULL, ints.eval = NULL, rit.param = list(depth = 5,
  ntree = 500, nchild = 2, class.id = 1, min.nd = 1, class.cut = NULL),
  varnames.grp = NULL, n.bootstrap = 1, bs.sample = NULL,
  weights = rep(1, nrow(x)), signed = TRUE, oob.importance = TRUE,
  type = "randomForest", n.core = 1, ...)

`x`	numeric feature matrix
`y`	response vector. If factor, classification is assumed.
`ntree`	number of random forest trees.
`mtry.select.prob`	feature weights for first iteration. Defaults to equal weights
`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.
`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.
`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.
`n.core`	number of cores to use. If -1, all available cores are used.
`...`	additional arguments passed to iRF::randomForest.