hypervolume-package: High Dimensional Geometry, Set Operations, Projection, and...
In bblonder/hypervolume: High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls
hypervolume-package R Documentation
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.
Details
A frequently asked questions document (FAQ) can be found at http://www.benjaminblonder.org/hypervolume_faq.html. More details are also available in a user guide within our 2018 paper (see reference below).
Author(s)
Benjamin Blonder, with contributions from Cecina Babich Morrow, Stuart Brown, Gregoire Butruille, Daniel Chen, Alex Laini, David J. Harris, and Clement Violet
Maintainer: Benjamin Blonder <benjamin.blonder@berkeley.edu>
References
Blonder, B., Lamanna, C., Violle, C. and Enquist, B. J. (2014), The n-dimensional hypervolume. Global Ecology and Biogeography, 23: 595-609. doi: 10.1111/geb.12146
Blonder, B. Do Hypervolumes Have Holes?, The American Naturalist, 187(4) E93-E105. doi: 10.1086/685444
Blonder, B., Morrow, C.B., Maitner, B., et al. New approaches for delineating n-dimensional hypervolumes. Methods Ecol Evol. 2018;9:305-319. doi: 10.1111/2041-210X.12865
bblonder/hypervolume documentation built on Jan. 21, 2025, 3:09 p.m.
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| hypervolume-package | R Documentation |
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.
Details
A frequently asked questions document (FAQ) can be found at http://www.benjaminblonder.org/hypervolume_faq.html. More details are also available in a user guide within our 2018 paper (see reference below).
Author(s)
Benjamin Blonder, with contributions from Cecina Babich Morrow, Stuart Brown, Gregoire Butruille, Daniel Chen, Alex Laini, David J. Harris, and Clement Violet
Maintainer: Benjamin Blonder <benjamin.blonder@berkeley.edu>
References
Blonder, B., Lamanna, C., Violle, C. and Enquist, B. J. (2014), The n-dimensional hypervolume. Global Ecology and Biogeography, 23: 595-609. doi: 10.1111/geb.12146
Blonder, B. Do Hypervolumes Have Holes?, The American Naturalist, 187(4) E93-E105. doi: 10.1086/685444
Blonder, B., Morrow, C.B., Maitner, B., et al. New approaches for delineating n-dimensional hypervolumes. Methods Ecol Evol. 2018;9:305-319. doi: 10.1111/2041-210X.12865
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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