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.


Reference manual

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3.0.1 by Benjamin Blonder, 20 days ago

Browse source code at https://github.com/cran/hypervolume

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

Documentation:   PDF Manual  

GPL-3 license

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

Depends on Rcpp, methods

Suggests rgl, magick, alphahull, knitr, rmarkdown, gridExtra

Linking to Rcpp, RcppArmadillo, progress

Imported by BAT, Ostats, cati, raptr.

Suggested by ENMTools, TreeDist.

See at CRAN