Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
|Author||Benjamin Blonder, with contributions from David J. Harris|
|Maintainer||Benjamin Blonder <email@example.com>|
|Package repository||View on GitHub|
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