hypervolume_redundancy

Estimates squared probability density at a given point. This metric is proportional to the number of data points multiplied by the probability density at a point.

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.

Benjamin Blonder

hypervolume

High Dimensional Geometry, Set Operations, Projection, and
Inference Using Kernel Density Estimation, Support Vector
Machines, and Convex Hulls

hypervolume_redundancy function

<dl> <dt>...</dt>
<dd>Arguments to be passed to <code>hypervolume_estimate_probability</code></dd></dl>

Arguments

Redundancy of a point in a hypervolume — hypervolume_redundancy

<dl>

 <dt>...</dt>
<dd>Arguments to be passed to <code>hypervolume_estimate_probability</code></dd>

</dl>

hypervolume_redundancy: Redundancy of a point in a hypervolume

Description

Usage

Arguments

See Also