Package: GWlasso 1.0.1

Matthieu Mulot

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>.

Authors:Matthieu Mulot [aut, cre, cph], Sophie Erb [aut]

GWlasso_1.0.1.tar.gz
GWlasso_1.0.1.tar.gz(r-4.5-noble)GWlasso_1.0.1.tar.gz(r-4.4-noble)
GWlasso_1.0.1.tgz(r-4.4-emscripten)GWlasso_1.0.1.tgz(r-4.3-emscripten)
GWlasso.pdf |GWlasso.html✨
GWlasso/json (API)
NEWS

# Install 'GWlasso' in R:
install.packages('GWlasso', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/nibortolum/gwlasso/issues0 issues

Pkgdown site:https://nibortolum.github.io

Datasets:
  • Amesbury - Amesbury Testate Amoebae dataset

On CRAN:

Conda:

2.70 score 155 downloads 5 exports 80 dependencies

Last updated 4 months agofrom:eb81ae9a6a. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 23 2025
R-4.5-linuxOKMar 23 2025
R-4.4-linuxOKMar 23 2025

Exports:%>%compute_distance_matrixgwl_bw_estimationgwl_fitplot_gwl_map

Dependencies:bootclassclassIntclicodacodetoolscolorspacecpp11crayonDBIdeldirDEoptimRdplyre1071fansifarverFNNforeachgenericsggplot2ggsideglmnetgluegtableGWmodelhmsintervalsisobanditeratorsKernSmoothlabelinglatticeLearnBayeslifecyclemagrittrMASSMatrixmgcvmultcompmunsellmvtnormnlmepillarpkgconfigprettyunitsprogressproxypurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrlangrobustbases2sandwichscalessfshapespspacetimespatialregspDataspdepstringistringrsurvivalTH.datatibbletidyrtidyselectunitsutf8vctrsviridisLitewithrwkxtszoo

Example analysis

Rendered fromexample_analysis.Rmdusingknitr::rmarkdownon Mar 23 2025.

Last update: 2024-11-22
Started: 2024-11-22

Citation

To cite package ‘GWlasso’ in publications use:

Mulot M, Erb S (2024). GWlasso: Geographically Weighted Lasso. R package version 1.0.1, https://CRAN.R-project.org/package=GWlasso.

Corresponding BibTeX entry: 

  @Manual{,
    title = {GWlasso: Geographically Weighted Lasso},
    author = {Matthieu Mulot and Sophie Erb},
    year = {2024},
    note = {R package version 1.0.1},
    url = {https://CRAN.R-project.org/package=GWlasso},
  }

Readme and manuals

GWlasso

The goal of GWlasso is to provides a set of functions to perform Geographically weighted lasso. It was originally thought to be used in palaeoecological settings but can be used to other extents.

The package has been submitted to CRAN and is awaiting evaluation

Installation

You can install the development version of GWlasso from GitHub with:

# install.packages("devtools")
devtools::install_github("nibortolum/GWlasso")

Example

This is a basic example on how to run a GWlasso pipeline:

library(GWlasso)

## compute a distance matrix from a set of coordinates
distance_matrix <- compute_distance_matrix <- function(coords, method = "euclidean", add.noise = FALSE)

## compute the optimal bandwidth 
  myst.est <- gwl_bw_estimation(x.var = predictors_df, 
                              y.var = y_vector,
                              dist.mat = distance_matrix,
                              adaptive = TRUE,
                              adptbwd.thresh = 0.1,
                              kernel = "bisquare",
                              alpha = 1,
                              progress = TRUE,
                              n=40,
                              nfolds = 5)

## Compute the optimal model
my.gwl.fit <- gwl_fit(myst.est$bw,
                      x.var = data.sample[,-1], 
                      y.var = data.sample$WTD,
                      kernel = "bisquare",
                      dist.mat = distance_matrix, 
                      alpha = 1, 
                      adaptive = TRUE, progress = T)

## make predictions 

predicted_values <- predict(my.gwl.fit, newdata = new_data, newcoords = new_coords)