For each point in one data set, calculate distance to nearest point in second data set

Trying to find, for each point in a SpatialPointsDataFrame, the distance to the closest point in a second SpatialPointsDataFrame (equivalent to the "nearest" tool in ArcGIS for two SpatialPointDataFrames).

I can do the naive implementation by calculating all pairwise distances using gDistance and taking the min (like answer 1 here), but I have some huge datasets and was looking for something more efficient.

For example, here's a trick with knearneigh for points in same dataset.

Cross-posted on r-sig-geo

The SearchTrees package offers one solution. Quoting from its documentation, it, "provides an implementation of the QuadTree data structure [which it] uses to implement fast k-Nearest Neighbor [...] lookups in two dimensions."

Here's how you could use it to quickly find, for each point in a SpatialPoints object b, the two nearest points in a second SpatialPoints object B

library(sp)
library(SearchTrees)

## Example data
set.seed(1)
A <- SpatialPoints(cbind(x=rnorm(100), y=rnorm(100)))
B <- SpatialPoints(cbind(x=c(-1, 0, 1), y=c(1, 0, -1)))

## Find indices of the two nearest points in A to each of the points in B
tree <- createTree(coordinates(A))
inds <- knnLookup(tree, newdat=coordinates(B), k=2)

## Show that it worked
plot(A, pch=1, cex=1.2)
points(B, col=c("blue", "red", "green"), pch=17, cex=1.5)
## Plot two nearest neigbors
points(A[inds[1,],], pch=16, col=adjustcolor("blue", alpha=0.7))
points(A[inds[2,],], pch=16, col=adjustcolor("red", alpha=0.7))
points(A[inds[3,],], pch=16, col=adjustcolor("green", alpha=0.7))