_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-hypervolume 3.1.5
Propagated dependencies: r-terra@1.8-50 r-sp@2.2-0 r-rcpparmadillo@14.4.2-1 r-rcpp@1.0.14 r-raster@3.6-32 r-purrr@1.0.4 r-progress@1.2.3 r-pdist@1.2.1 r-pbapply@1.7-2 r-palmerpenguins@0.1.1 r-mvtnorm@1.3-3 r-mass@7.3-65 r-maps@3.4.2.1 r-ks@1.15.1 r-hitandrun@0.5-6 r-ggplot2@3.5.2 r-geometry@0.5.2 r-foreach@1.5.2 r-fastcluster@1.3.0 r-e1071@1.7-16 r-dplyr@1.1.4 r-doparallel@1.0.17 r-data-table@1.17.2 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://github.com/bblonder/hypervolume
Licenses: GPL 3
Synopsis: High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls
Description:

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.

Total results: 1