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 version≥ 3.5.0
- LicenseGPL-3
- Needs compilation?Yes
- Last release05/01/2024
Documentation
Team
Benjamin Blonder
Benjamin Blonder, with contributions from Cecina Babich Morrow, Stuart Brown, Gregoire Butruille, Daniel Chen, Alex Laini, and David J. Harris
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- Depends3 packages
- Imports24 packages
- Suggests6 packages
- Linking To3 packages
- Reverse Imports4 packages
- Reverse Suggests2 packages