pMEM: Predictive Moran's Eigenvector Maps

Calculation of Predictive Moran's eigenvector maps (pMEM), as defined by Guénard and Legendre (In Press) "Spatially-explicit predictions using spatial eigenvector maps" <doi:10.5281/zenodo.13356457>. Methods in Ecology and Evolution. This method enables scientists to predict the values of spatially-structured environmental variables. Multiple types of pMEM are defined, each one implemented on the basis of spatial weighting function taking a range parameter, and sometimes also a shape parameter. The code's modular nature enables programers to implement new pMEM by defining new spatial weighting functions.

Version:	0.1-1
Depends:	R (≥ 3.5.0), sf
Imports:	Rcpp (≥ 1.0.11)
LinkingTo:	Rcpp
Suggests:	knitr, xfun, magrittr, glmnet
Published:	2024-09-30
DOI:	10.32614/CRAN.package.pMEM
Author:	Guillaume Guénard [aut, cre], Pierre Legendre [ctb]
Maintainer:	Guillaume Guénard <guillaume.guenard at umontreal.ca>
License:	GPL-3
NeedsCompilation:	yes
Citation:	pMEM citation info
CRAN checks:	pMEM results

Documentation:

Reference manual:	pMEM.pdf
Vignettes:	Using pMEM for Spatial Modelling with Predictive Moran's Eigenvector Maps (source, R code)

Downloads:

Package source:	pMEM_0.1-1.tar.gz
Windows binaries:	r-devel: pMEM_0.1-1.zip, r-release: pMEM_0.1-1.zip, r-oldrel: pMEM_0.1-1.zip
macOS binaries:	r-release (arm64): pMEM_0.1-1.tgz, r-oldrel (arm64): pMEM_0.1-1.tgz, r-release (x86_64): pMEM_0.1-1.tgz, r-oldrel (x86_64): pMEM_0.1-1.tgz

Linking:

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