hypervolume (2.0.12)

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High Dimensional Geometry and Set Operations 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.

Maintainer: Benjamin Blonder
Author(s): Benjamin Blonder, with contributions from David J. Harris

License: GPL-3

Uses: data.table, e1071, fastcluster, geometry, hitandrun, ks, maps, MASS, mvtnorm, pdist, progress, raster, Rcpp, rgeos, rgl, sp, alphahull, magick

Released about 1 month ago.

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