GWlasso: Geographically Weighted Lasso

Performs geographically weighted Lasso regressions. Find optimal bandwidth, fit a geographically weighted lasso or ridge regression, and make predictions. These methods are specially well suited for ecological inferences. Bandwidth selection algorithm is from A. Comber and P. Harris (2018) <doi:10.1007/s10109-018-0280-7>.

Version: 1.0.2
Depends: R (≥ 3.5.0)
Imports: dplyr, ggplot2, ggside, glmnet, GWmodel, lifecycle, magrittr, methods, progress, rlang, sf, tidyr
Suggests: knitr, maps, rmarkdown
Published: 2025-09-26
DOI: 10.32614/CRAN.package.GWlasso
Author: Matthieu Mulot ORCID iD [aut, cre, cph], Sophie Erb ORCID iD [aut]
Maintainer: Matthieu Mulot <matthieu.mulot at gmail.com>
BugReports: https://github.com/nibortolum/GWlasso/issues
License: MIT + file LICENSE
URL: https://github.com/nibortolum/GWlasso, https://nibortolum.github.io/GWlasso/
NeedsCompilation: no
Citation: GWlasso citation info
Materials: README, NEWS
CRAN checks: GWlasso results

Documentation:

Reference manual: GWlasso.html , GWlasso.pdf
Vignettes: example_analysis (source, R code)

Downloads:

Package source: GWlasso_1.0.2.tar.gz
Windows binaries: r-devel: GWlasso_1.0.2.zip, r-release: GWlasso_1.0.2.zip, r-oldrel: GWlasso_1.0.2.zip
macOS binaries: r-release (arm64): GWlasso_1.0.2.tgz, r-oldrel (arm64): GWlasso_1.0.2.tgz, r-release (x86_64): GWlasso_1.0.2.tgz, r-oldrel (x86_64): GWlasso_1.0.2.tgz
Old sources: GWlasso archive

Linking:

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