lagr.tune.bw: Evaluate the bandwidth selection criterion for a given...
In wrbrooks/lagr: Local adaptive grouped regularization

Evaluate the bandwidth selection criterion for a given bandwidth

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lagr.tune.bw(x, y, weights, coords, dist, family, bw, kernel, env, oracle,
  varselect.method, tol.loc, bw.type, bwselect.method, min.dist, max.dist,
  lambda.min.ratio, n.lambda, lagr.convergence.tol, lagr.max.iter, verbose)

`weights`	vector of prior observation weights (due to, e.g., overdispersion). Not related to the kernel weights.
`coords`	matrix of locations, with each row giving the location at which the corresponding row of data was observed
`family`	exponential family distribution of the response
`bw`	bandwidth for the kernel
`kernel`	kernel function for generating the local observation weights
`varselect.method`	criterion to minimize in the regularization step of fitting local models - options are `AIC`, `AICc`, `BIC`, `GCV`
`tol.loc`	tolerance for the tuning of an adaptive bandwidth (e.g. `knn` or `nen`)
`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)
`bwselect.method`	criterion to minimize when tuning bandwidth - options are `AICc`, `BICg`, and `GCV`
`verbose`	print detailed information about our progress?
`formula`	symbolic representation of the model
`data`	data frame containing observations of all the terms represented in the formula
`longlat`	`TRUE` indicates that the coordinates are specified in longitude/latitude, `FALSE` indicates Cartesian coordinates. Default is `FALSE`.