hypervolume: 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.

Version: 2.0.7
Depends: Rcpp, rgl, methods, R (≥ 3.0.0)
Imports: raster, maps, MASS, geometry, ks, pdist, fastcluster, compiler, e1071, hitandrun, progress, mvtnorm, data.table, rgeos, sp
LinkingTo: Rcpp, RcppArmadillo, progress
Suggests: magick, alphahull
Published: 2017-08-07
Author: Benjamin Blonder, with contributions from David J. Harris
Maintainer: Benjamin Blonder <bblonder at gmail.com>
License: GPL-3
NeedsCompilation: yes
CRAN checks: hypervolume results


Reference manual: hypervolume.pdf
Package source: hypervolume_2.0.7.tar.gz
Windows binaries: r-devel: hypervolume_2.0.7.zip, r-release: hypervolume_2.0.7.zip, r-oldrel: hypervolume_2.0.7.zip
OS X El Capitan binaries: r-release: hypervolume_2.0.7.tgz
OS X Mavericks binaries: r-oldrel: hypervolume_2.0.7.tgz
Old sources: hypervolume archive

Reverse dependencies:

Reverse imports: cati, raptr


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