sgvSetup: One-time setup wrapper function for the SGV approximation
In BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models

sgvSetup is a wrapper function that sets up the SGV approximation. Three objects are required: (1) ordering the locations, (2) identify nearest neighbors, and (3) determine the conditioning set. This function only needs to be run once per SGV analysis.

sgvSetup(
  coords,
  coords_pred = NULL,
  k = 15,
  seed = NULL,
  pred.seed = NULL,
  order_coords = TRUE
)

`coords`	Matrix of observed locations.
`coords_pred`	Optional matrix of prediction locations.
`k`	Number of neighbors.
`seed`	Setting the seed for reproducibility of the observed location ordering
`pred.seed`	Setting the seed for reproducibility of the prediction ordering.
`order_coords`	Logical; should the coordinates be ordered.

A list with the following components:

`ord`	A vector of ordering position for the observed locations.
`ord_pred`	A vector of ordering position for the prediction locations (if `coords_pred` is provided).
`ord_all`	A concatenated vector of `ord` and `ord_pred`.
`coords_ord`	A matrix of ordered locations (observed and prediction), included for convenience.
`nID_ord`	A matrix of (ordered) neighbor indices.
`condition_on_y_ord`	A matrix indicating whether the conditioning set for each (ordered) location is on the latent process (y, `1`) or the observed values (z, `0`).