Wrap clustering_cv()

[mikemahoney218]

This fixes #120 and fixes #104 by wrapping rsample::clustering_cv().

There's three big (breaking) changes here that I'm aware of:

1. spatial_clustering_cv() no longer handles non-sf objects, because I think they'd be better handled via rsample::clustering_cv().
2. Distances are now calculated between edges, not centroids, of non-point geometries. This is how the rest of the package works, and it makes the most sense to me; if you have polygons which touch, you'd probably assume their data has some amount of spatial relationship, regardless of where the midpoint of each polygon is.
3. Because the new distance_function argument is now a function by default, and gets assigned as an attribute to the resulting rset, the distance_function attribute winds up having a somewhat complex environment, which is non-intuitive:

library(spatialsample)

clust <- spatial_clustering_cv(boston_canopy, v = 2)
lobstr::obj_sizes(
  boston_canopy,
  environment(attr(clust, "distance_function"))
)
#> * 984.07 kB
#> *  60.74 kB

ls(environment(attr(clust, "distance_function")))
#>  [1] "buffer"            "cluster_function"  "cv_att"           
#>  [4] "data"              "distance_function" "n"                
#>  [7] "radius"            "repeats"           "rset"             
#> [10] "v"

^{Created on 2022-12-08 by the reprex package (v2.0.1)}

I'm not sure if there's a good way to "zero out" that environment, so that we aren't accidentally dragging extra data along.

Otherwise, this function should work the same as it always has.

[hfrick]

That looks good to me! Just a minor comment on the docs

[hfrick]

this leads to the docs for distance_function to state that the default would be stats::dist()

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