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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

295

Version

1.2-3

License

GPL-3

Maintainer

Guillaume Guenard

Last Published

April 16th, 2024

Functions in codep (1.2-3)

LGTransforms

Transformation for Species Abundance Data
mite

The Oribatid Mite Data Set
product-distribution

Frequency Distributions for MCA Parametric Testing
geodesics

Calculation of Geodesic Distances
minpermute

Number of Permutations for MCA
weighting-functions

Weighting Functions for Spatial Eigenvector Map
salmon

The St. Marguerite River Altantic Salmon Parr Transect
LGDat

Legendre and Gallagher Synthetic Example
Euclid

Calculation of the Euclidean Distance
cthreshold

Familywise Type I Error Rate
codep-package

tools:::Rd_package_title("codep")
MCA

Multiple-descriptors, Multiscale Codependence Analysis
eigenmap-class

Class and Methods for Spatial Eigenvector Maps
cdp-class

Class and Methods for Multiscale Codependence Analysis (MCA)
Doubs

The Doubs Fish Data
eigenmap

Spatial Eigenvector Maps