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

A script that validates that data inputs are correct, and returns a X distance and Y distance matrix for MGC.

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

`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`	`[n]` a vector containing the sample ids for our `n` samples.
`is.dist.X`	a boolean indicating whether your `X` input is a distance matrix or not. Defaults to `FALSE`.
`dist.xfm.X`	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.X`	a list of trailing arguments to pass to the distance function specified in `dist.xfm.X`. Defaults to `list(method='euclidean')`.
`dist.return.X`	the return argument for the specified `dist.xfm.X` 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.X[[dist.return]]` as the distance matrix. Should be a `[n x n]` matrix.
`is.dist.Y`	a boolean indicating whether your `Y` input is a distance matrix or not. Defaults to `FALSE`.
`dist.xfm.Y`	if `is.dist == FALSE`, a distance function to transform `Y`. 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 `dist.return.Y` return argument. See mgc.distance for details.
`dist.params.Y`	a list of trailing arguments to pass to the distance function specified in `dist.xfm.Y`. Defaults to `list(method='euclidean')`.
`dist.return.Y`	the return argument for the specified `dist.xfm.Y` containing the distance matrix. Defaults to `FALSE`. `is.null(dist.return)` use the return argument directly from `dist.xfm.Y(Y)` as the distance matrix. Should be a `[n x n]` matrix. `is.character(dist.return) \| is.integer(dist.return)` use `dist.xfm.Y(Y)[[dist.return]]` as the distance matrix. Should be a `[n x n]` matrix.