lagr.ssr: Calculate the sum of squared residuals for a local model
In wrbrooks/lagr: Local adaptive grouped regularization

This function fits a local LAGR model at loc, and returns its sum of squared residuals (SSR) as a proportion of the SSR from a global model. This proportion is how the bandwidth is specified under nen.

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lagr.ssr(bw, x, y, group.id, family, loc, coords, dist, kernel, target,
  varselect.method, prior.weights, oracle, verbose, lambda.min.ratio, n.lambda,
  lagr.convergence.tol, lagr.max.iter)

`bw`	kernel bandwidth (distance) to use for fitting the local model
`x`	matrix of observed covariates
`y`	vector of observed responses
`family`	exponential family distribution of the response
`loc`	location around which to center the kernel
`coords`	matrix of locations, with each row giving the location at which the corresponding row of data was observed
`dist`	vector of distances from central location to the observation locations
`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`
`verbose`	print detailed information about our progress?
`longlat`	`TRUE` indicates that the coordinates are specified in longitude/latitude, `FALSE` indicates Cartesian coordinates. Default is `FALSE`.
`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)
`tol.loc`	tolerance for the tuning of an adaptive bandwidth (e.g. `knn` or `nen`)
`tuning`	logical indicating whether this model will be used to tune the bandwidth, in which case only the tuning criteria are returned
`D`	pre-specified matrix of distances between locations