hypervolume-package: High Dimensional Geometry, Set Operations, Projection, and...

hypervolume-packageR Documentation

High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls

Description

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.

Details

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).

Author(s)

Benjamin Blonder, with contributions from Cecina Babich Morrow, David J. Harris, Stuart Brown, Gregoire Butruille, Alex Laini, and Dan Chen

Maintainer: Benjamin Blonder <benjamin.blonder@berkeley.edu>

References

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


hypervolume documentation built on May 17, 2022, 1:06 a.m.