hypervolume-package

tools:::Rd_package_description("hypervolume")

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.

Benjamin Blonder

hypervolume

High Dimensional Geometry, Set Operations, Projection, and
Inference Using Kernel Density Estimation, Support Vector
Machines, and Convex Hulls

hypervolume-package function

tools:::Rd_package_author("hypervolume")
Maintainer: tools:::Rd_package_maintainer("hypervolume")

hypervolume-package: tools:::Rd_package_title("hypervolume")

Description

Arguments

Author

Details

References