High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls R package hypervolume version 3.1.6 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. Software Depends: Rcpp, methods, R (>= 3.5.0) Imports: raster, maps, MASS, geometry, ks, hitandrun, pdist, fastcluster, compiler, e1071, progress, mvtnorm, data.table, terra, sp, foreach, doParallel, parallel, ggplot2, pbapply, palmerpenguins, purrr, dplyr, caret LinkingTo: Rcpp, RcppArmadillo, progress Suggests: rgl, magick, alphahull, knitr, rmarkdown, gridExtra Benjamin Blonder <benjamin.blonder@berkeley.edu> Comprehensive R Archive Network (CRAN) Benjamin Blonder [aut, cre], Cecina Babich Morrow [aut], Stuart Brown [aut], Gregoire Butruille [aut], Daniel Chen [aut], Alex Laini [aut], David J. Harris [aut], Clement Violet [aut] GPL-3 2025-06-15 application/tgz https://CRAN.R-project.org/package=hypervolume doi:10.32614/CRAN.package.hypervolume