It was developed by Michael Murphy as his masters project under Steven Skiena at Stony Brook. The choice of partition plane is not orthogonal, rather, it is selected by the principal eigenvector of the covariance matrix of the points. Timing data and algorithm animations can be used to study the performance of these data structures. Because kd-trees are heuristic data structures, their performance can be dramatically effected by the distribution of the data.
Ranger provides a number of features to aid in visualizing multi-dimensional data.
In any three-dimensional projections, rotation along arbitrary axes can be used to facilitate understanding. For each of the four search data structures, a graphic representation of the space division is provided. This provides considerable insight into the appropriateness of a data structure for a particular distribution. Ideally, the search space is decomposed into fat, regularly-sized regions, but this is often not possible with degenerate distributions. Ranger is written in C, using Motif.