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hypervolume (version 1.2.2)

High-Dimensional Kernel Density Estimation and Geometry Operations

Description

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.

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Version

Install

install.packages('hypervolume')

Monthly Downloads

2,147

Version

1.2.2

License

GPL-3

Maintainer

Benjamin Blonder

Last Published

March 12th, 2015

Functions in hypervolume (1.2.2)

Hypervolume-class

Class hypervolume
quercus

Data and demo for Quercus (oak) tree distributions
hypervolume

Hypervolume construction
hypervolume-package

N-dimensional hypervolume operations
hypervolume_join

Concatenate hypervolumes
estimate_bandwidth

Silverman bandwidth estimator for hypervolumes.
HypervolumeList-class

Class HypervolumeList
hypervolume_distance

Distance between two hypervolumes
hypervolume_importance

Hypervolume variable importance
negative_features

Negative feature detection
expectation_box

Hyperbox, hyperball expectation
get_volume

Extract volume
expectation_maximal

Maximal expectation
plot.HypervolumeList

Plot a hypervolume or list of hypervolumes
expectation_convex

Convex expectation
finch

Data and demo for Darwin's finches
summary.Hypervolume

Summary of hypervolume
hypervolume_set

Set operations (intersection / union / unique components)
hypervolume_sorensen_overlap

Sorensen similarity index for hypervolume set operations
hypervolume_inclusion_test

Inclusion test