lagr.dispatch: Dispatch the fitting of local models to parallel cores, if...
In wrbrooks/lagr: Local adaptive grouped regularization

Loops through estimation locations with foreach, sending each local model to a core for fitting. If zero or one cores are registered, then foreach computes the local models sequentially.

lagr.dispatch(x, y, family, coords, fit.loc, oracle, D, bw, bw.type, verbose,
  varselect.method, prior.weights, tuning, predict, simulation, kernel, min.bw,
  max.bw, min.dist, max.dist, tol.loc, lambda.min.ratio, n.lambda,
  lagr.convergence.tol, lagr.max.iter, jacknife = FALSE,
  bootstrap.index = NULL)

`x`	matrix of observed covariates
`y`	vector of observed responses
`family`	exponential family distribution of the response
`coords`	matrix of locations, with each row giving the location at which the corresponding row of data was observed
`fit.loc`	matrix of locations where the local models should be fitted
`D`	pre-specified matrix of distances between locations
`bw`	bandwidth parameter
`bw.type`	type of bandwidth - options are `dist` for distance (the default), `knn` for nearest neighbors (bandwidth a proportion of `n`), and `nen` for nearest effective neighbors (bandwidth a proportion of the sum of squared residuals from a global model)
`verbose`	print detailed information about our progress?
`varselect.method`	criterion to minimize in the regularization step of fitting local models - options are `AIC`, `AICc`, `BIC`, `GCV`
`tuning`	logical indicating whether this model will be used to tune the bandwidth, in which case only the tuning criteria are returned
`kernel`	kernel function for generating the local observation weights
`tol.loc`	tolerance for the tuning of an adaptive bandwidth (e.g. `knn` or `nen`)