acacia_pinus

Data for occurrences of Acacia and Pinus species based on geographic observations.

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

acacia_pinus function

A <code>data.frame</code> with 37845 observations on the following 3 variables.<dl>
 <dt><code>Species</code></dt>
<dd>a <code>character</code> containing 139 unique values</dd> <dt><code>Latitude</code></dt>
<dd>a numeric vector</dd> <dt><code>Longitude</code></dt>
<dd>a numeric vector</dd> 
</dl>

Format

Data for Acacia and Pinus tree distributions — acacia_pinus

A <code>data.frame</code> with 37845 observations on the following 3 variables.<dl>
 <dt><code>Species</code></dt>
<dd>a <code>character</code> containing 139 unique values</dd>

 <dt><code>Latitude</code></dt>
<dd>a numeric vector</dd>

 <dt><code>Longitude</code></dt>
<dd>a numeric vector</dd>

 
</dl>

acacia_pinus: Data for Acacia and Pinus tree distributions

Description

Usage

Arguments

Format

References