circles

Data generated by picking 100 points randomly within a circle of radius 1. See the vignette on occupancy for information about its usage.

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

circles function

A <code>list</code> with 20 objects of class <code>matrix</code>. Each <code>matrix</code> contains 100 rows and 2 columns.<dl>
 <dt><code>[,1]</code></dt>
<dd>x coordinate</dd> <dt><code>[,2]</code></dt>
<dd>y coordinate</dd> 
</dl>

Format

Circles simulated dataset — circles

A <code>list</code> with 20 objects of class <code>matrix</code>. Each <code>matrix</code> contains 100 rows and 2 columns.<dl>
 <dt><code>[,1]</code></dt>
<dd>x coordinate</dd>

 <dt><code>[,2]</code></dt>
<dd>y coordinate</dd>

 
</dl>

circles: Circles simulated dataset

Description

Usage

Arguments

Format