Performance?

I'm curious, have you measured the performance of this r-tree? During my extensive nearest neighbor search experiments in the past, I did make an attempt at an r-tree, but I found at least my impelementation to provide far inferior performance to my bucket kd-tree.

Yes. Now this tree in battles against Diamond in average takes 56,800 ns for RS. I know that it's not completly correctly to compare them, but yours kD-Tree do kNN with same k in about 70,000-80,000 ns. RS & kNN are performed more than 3000 times with 30 trees with 1-5 dimensions and 0- ~3200 points.
I plan to try use RS with dinamically calculate hypercube side in gun, which, i hope will produce pretty equal results with kNN, and in this case i will publish comparsion of RS & kNN for gun

How about seeing how many points are returned by the RS, and seeing how long it takes with that many points for kNN in comparison?

I've been considering testing RS in DrussGT instead of all of those movement buffers, but I'm too busy at the moment...

I did the exactly same. Something like this: E[] arr1 = rTree.rangeSearch(getRange(currentLoc)); E[] arr2 = kDTree.nearestNeighbor(currentLoc, arr1.length)

I'm curious, the does the RS give the same results? Or does the kNN return points in a hypersphere, whereas the RS gives results in a hypercube?