discr.validator: Discriminability Utility Validator
In neurodata/r-mgc: Multiscale Graph Correlation

A script that validates that data inputs are correct, and returns a distance matrix and a ids vector.

discr.validator(
  X,
  Y,
  is.dist = FALSE,
  dist.xfm = mgc.distance,
  dist.params = list(method = "euclidean"),
  dist.return = NULL,
  remove.isolates = TRUE
)

`X`	is interpreted as: a `[n x d]` data matrix X is a data matrix with `n` samples in `d` dimensions, if flag `is.dist=FALSE`. a `[n x n]` distance matrix X is a distance matrix. Use flag `is.dist=TRUE`.
`Y`	is interpreted as: a `[n x d]` data matrix Y is a data matrix with `n` samples in `d` dimensions, if flag `is.dist=FALSE`. a `[n x n]` distance matrix Y is a distance matrix. Use flag `is.dist=TRUE`.
`is.dist`	a boolean indicating whether your `X` input is a distance matrix or not. Defaults to `FALSE`.
`dist.xfm`	if `is.dist == FALSE`, a distance function to transform `X`. If a distance function is passed, it should accept an `[n x d]` matrix of `n` samples in `d` dimensions and return a `[n x n]` distance matrix as the `$D` return argument. See mgc.distance for details.
`dist.params`	a list of trailing arguments to pass to the distance function specified in `dist.xfm`. Defaults to `list(method='euclidean')`.
`dist.return`	the return argument for the specified `dist.xfm` containing the distance matrix. Defaults to `FALSE`. `is.null(dist.return)` use the return argument directly from `dist.xfm` as the distance matrix. Should be a `[n x n]` matrix. `is.character(dist.return) \| is.integer(dist.return)` use `dist.xfm[[dist.return]]` as the distance matrix. Should be a `[n x n]` matrix.
`remove.isolates`	whether to remove isolated samples, or samples with only a single instance in the `Y` vector.