pcds-package | R Documentation |
pcds
is a package for construction and
visualization of proximity catch digraphs (PCDs)
and computation of two graph invariants of the PCDs and
testing spatial patterns using these invariants.
The PCD families considered are Arc-Slice (AS) PCDs, Proportional-Edge (PE) PCDs and Central Similarity (CS) PCDs.
The graph invariants used in testing spatial point data are the domination number (\insertCiteceyhan:dom-num-NPE-Spat2011;textualpcds) and arc density (\insertCiteceyhan:arc-density-PE,ceyhan:arc-density-CS;textualpcds) of for two-dimensional data.
The pcds
package also contains the functions
for generating patterns of segregation, association, CSR
(complete spatial randomness) and Uniform data
in one, two and three dimensional cases,
for testing these patterns
based on two invariants of various families of the proximity catch digraphs (PCDs),
(see (\insertCiteceyhan:Phd-thesis;textualpcds).
Moreover, the package has visualization tools for these digraphs for 1D-3D vertices. The AS-PCD and CS-PCD tools are provided for 1D and 2D data and PE-PCD related tools are provided for 1D-3D data.
pcds
functionsThe pcds
functions can be grouped as
Auxiliary Functions,
AS-PCD Functions,
PE-PCD Functions,
and CS-PCD Functions.
Contains the auxiliary (or utility) functions for constructing and visualizing Delaunay tessellations in 1D and 2D settings, computing the domination number, constructing the geometrical tools, such as equation of lines for two points, distances between lines and points, checking points inside the triangle etc., finding the (local) extrema (restricted to Delaunay cells or vertex or edge regions in them).
Contains the functions used in AS-PCD construction, estimation of domination number, arc density, etc in the 2D setting.
Contains the functions used in PE-PCD construction, estimation of domination number, arc density, etc in the 1D-3D settings.
Contains the functions used in CS-PCD construction, estimation of domination number, arc density, etc in the 1D and 2D setting.
Contains functions for generation of points from uniform (or CSR), segregation and association patterns.
In all these functions points are vectors, and data sets are either matrices or data frames.
Maintainer: Elvan Ceyhan elvanceyhan@gmail.com
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