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R Package codep

Computation of Multiscale Codependence Analysis (MCA) and eigenvector maps, as an additional feature.

MCA is useful study the relationships between variables that are expected to occur at specific (spatial, temporal, phylogenetic, and so on) scales.

MCA quantifies and test the joint spatial/temporal trends between variables. These trends are described by eigenvector maps. The analysis may involve one or more response variable(s) and multiple scales. The explanatory variables are related to the response through an exclusive set of the eigenvectors in the map.

Maintained by Guillaume Guénard -- Université de Montréal

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Version

Install

install.packages('codep')

Monthly Downloads

573

Version

1.2-4

License

GPL-3

Last Published

September 23rd, 2024

Functions in codep (1.2-4)

minpermute

Number of Permutations for MCA
geodesics

Calculation of Geodesic Distances
product-distribution

Frequency Distributions for MCA Parametric Testing
mite

The Oribatid Mite Data Set
weighting-functions

Weighting Functions for Spatial Eigenvector Map
LGTransforms

Transformation for Species Abundance Data
Doubs

The Doubs Fish Data
MCA

Multiple-descriptors, Multiscale Codependence Analysis
LGDat

Legendre and Gallagher Synthetic Example
codep_PACKAGE

tools:::Rd_package_title("codep")
Euclid

Calculation of the Euclidean Distance
salmon

The St. Marguerite River Altantic Salmon Parr Transect
eigenmap-class

Class and Methods for Spatial Eigenvector Maps
cthreshold

Familywise Type I Error Rate
cdp-class

Class and Methods for Multiscale Codependence Analysis (MCA)
eigenmap

Spatial Eigenvector Maps