hypervolume-package {hypervolume}R Documentation

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.


A frequently asked questions document (FAQ) can be found at http://www.benjaminblonder.org/hypervolume_faq.html. More details are also available in a user guide within our 2018 paper (see reference below).


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>


Blonder, B., Lamanna, C., Violle, C. and Enquist, B. J. (2014), The n-dimensional hypervolume. Global Ecology and Biogeography, 23: 595-609. doi: 10.1111/geb.12146

Blonder, B. Do Hypervolumes Have Holes?, The American Naturalist, 187(4) E93-E105. doi: 10.1086/685444

Blonder, B., Morrow, C.B., Maitner, B., et al. New approaches for delineating n-dimensional hypervolumes. Methods Ecol Evol. 2018;9:305-319. doi: 10.1111/2041-210X.12865

[Package hypervolume version 3.1.3 Index]