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.


Reference manual

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2.0.7 by Benjamin Blonder, 3 months ago

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

Authors: Benjamin Blonder, with contributions from David J. Harris

Documentation:   PDF Manual  

GPL-3 license

Imports raster, maps, MASS, geometry, ks, pdist, fastcluster, compiler, e1071, hitandrun, progress, mvtnorm, data.table, rgeos, sp

Depends on Rcpp, rgl, methods

Suggests magick, alphahull

Linking to Rcpp, RcppArmadillo, progress

Imported by cati, raptr.

See at CRAN