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.

Version: 3.0.0
Depends: Rcpp, methods, R (≥ 3.0.2)
Imports: raster, maps, MASS, geometry, ks, pdist, fastcluster, compiler, e1071, hitandrun, progress, mvtnorm, data.table, rgeos, sp, foreach, doParallel, parallel, ggplot2, pbapply, palmerpenguins, purrr, dplyr, caret
LinkingTo: Rcpp, RcppArmadillo, progress
Suggests: rgl, magick, alphahull, knitr, rmarkdown, gridExtra
Published: 2021-11-12
Author: 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 at berkeley.edu>
License: GPL-3
NeedsCompilation: yes
CRAN checks: hypervolume results

Documentation:

Reference manual: hypervolume.pdf
Vignettes: Hypervolume-Resampling

Downloads:

Package source: hypervolume_3.0.0.tar.gz
Windows binaries: r-devel: hypervolume_3.0.0.zip, r-devel-UCRT: hypervolume_3.0.0.zip, r-release: hypervolume_3.0.0.zip, r-oldrel: hypervolume_3.0.0.zip
macOS binaries: r-release (arm64): hypervolume_3.0.0.tgz, r-release (x86_64): hypervolume_3.0.0.tgz, r-oldrel: hypervolume_3.0.0.tgz
Old sources: hypervolume archive

Reverse dependencies:

Reverse imports: BAT, cati, Ostats, raptr
Reverse suggests: ENMTools

Linking:

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