hypervolume-package: High Dimensional Geometry, Set Operations, Projection, and...
In 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 [aut, cre],
Cecina Babich Morrow [aut],
Stuart Brown [aut],
Gregoire Butruille [aut],
Daniel Chen [aut],
Alex Laini [aut],
David J. Harris [aut],
Clement Violet [aut]
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
hypervolume documentation built on April 4, 2025, 12:46 a.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 [aut, cre], Cecina Babich Morrow [aut], Stuart Brown [aut], Gregoire Butruille [aut], Daniel Chen [aut], Alex Laini [aut], David J. Harris [aut], Clement Violet [aut]
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
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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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