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hypervolume: High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls
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.
Getting started
Browse package contents
Package details |
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| Author | Benjamin Blonder, with contributions from Cecina Babich Morrow, Stuart Brown, Gregoire Butruille, Daniel Chen, Alex Laini, and David J. Harris |
| Maintainer | Benjamin Blonder <benjamin.blonder@berkeley.edu> |
| License | GPL-3 |
| Version | 3.1.3 |
| Package repository | View on CRAN |
| Installation |
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