StepGWR: A Hybrid Spatial Model for Prediction and Capturing Spatial Variation in the Data

It is a hybrid spatial model that combines the variable selection capabilities of stepwise regression methods with the predictive power of the Geographically Weighted Regression(GWR) model.The developed hybrid model follows a two-step approach where the stepwise variable selection method is applied first to identify the subset of predictors that have the most significant impact on the response variable, and then a GWR model is fitted using those selected variables for spatial prediction at test or unknown locations. For method details,see Leung, Y., Mei, C. L. and Zhang, W. X. (2000).<doi:10.1068/a3162>.This hybrid spatial model aims to improve the accuracy and interpretability of GWR predictions by selecting a subset of relevant variables through a stepwise selection process.This approach is particularly useful for modeling spatially varying relationships and improving the accuracy of spatial predictions.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: stats, qpdf, numbers, MASS
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-05-15
DOI: 10.32614/CRAN.package.StepGWR
Author: Nobin Chandra Paul [aut, cre, cph], Moumita Baishya [aut]
Maintainer: Nobin Chandra Paul <nobin.paul at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
NeedsCompilation: no
CRAN checks: StepGWR results


Reference manual: StepGWR.pdf


Package source: StepGWR_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): StepGWR_0.1.0.tgz, r-oldrel (arm64): StepGWR_0.1.0.tgz, r-release (x86_64): StepGWR_0.1.0.tgz, r-oldrel (x86_64): StepGWR_0.1.0.tgz


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