Package: StepGWR
Type: Package
Title: A Hybrid Spatial Model for Prediction and Capturing Spatial
        Variation in the Data
Version: 0.1.0
Authors@R: c(person("Nobin Chandra","Paul", role=c("aut","cre","cph"), email="nobin.paul@icar.gov.in"),person("Moumita","Baishya",role="aut"))
Depends: R(>= 2.10)
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Description: 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.
License: GPL (>= 2.0)
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: stats, qpdf, numbers,MASS
NeedsCompilation: no
Packaged: 2023-05-15 10:35:54 UTC; nobin ch paul
Author: Nobin Chandra Paul [aut, cre, cph],
  Moumita Baishya [aut]
Maintainer: Nobin Chandra Paul <nobin.paul@icar.gov.in>
Repository: CRAN
Date/Publication: 2023-05-15 19:10:16 UTC
Built: R 4.6.0; ; 2026-04-26 02:04:10 UTC; unix
