Package: GWmodel
Type: Package
Version: 2.3-2
Date: 2024-01-30
Title: Geographically-Weighted Models
Depends: R (>= 3.0.0), robustbase,sp (> 1.4-0),Rcpp
Imports: methods, sf, grDevices, spacetime,spdep,spatialreg,FNN
LinkingTo: Rcpp, RcppArmadillo
Suggests: mvoutlier, RColorBrewer, gstat,spData
Description: Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.
Author: Binbin Lu[aut], Paul Harris[aut], Martin Charlton[aut], Chris Brunsdon[aut], Tomoki Nakaya[aut], Daisuke Murakami[aut],Isabella Gollini[ctb], Yigong Hu[ctb], Fiona H Evans[ctb]
Maintainer: Binbin Lu <binbinlu@whu.edu.cn>
License: GPL (>= 2)
Repository: CRAN
URL: http://gwr.nuim.ie/
NeedsCompilation: yes
SystemRequirements: GNU make
Packaged: 2024-01-30 09:38:11 UTC; Lenovo
Date/Publication: 2024-01-31 16:30:06 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 02:50:24 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
