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.
- Version3.1.4
- R versionunknown
- LicenseGPL-3
- Needs compilation?Yes
- Last release05/01/2024
Documentation
Team
Benjamin Blonder
David J. Harris
Show author detailsRolesContributorCecina Babich Morrow
Show author detailsRolesContributorStuart Brown
Gregoire Butruille
Show author detailsRolesContributorDaniel Chen
Show author detailsRolesContributorAlex Laini
Show author detailsRolesContributor
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