hypervolume: High-dimensional Kernel Density Estimation and Geometry
Operations
Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and negative feature detection. Uses stochastic geometry approach to high-dimensional kernel density estimation. Builds n-dimensional convex hulls (polytopes). Can measure the n-dimensional ecological hypervolume and perform species distribution modeling.
Version: |
1.1.2 |
Depends: |
Rcpp, rgl, methods, R (≥ 3.0.0) |
Imports: |
MASS, geometry, pdist |
LinkingTo: |
Rcpp |
Published: |
2015-01-16 |
Author: |
Benjamin Blonder |
Maintainer: |
Benjamin Blonder <bblonder at gmail.com> |
License: |
GPL-3 |
NeedsCompilation: |
yes |
CRAN checks: |
hypervolume results |
Downloads:
Reverse dependencies: