Depends: R (>= 3.0.0),maptools (>= 0.5-2), robustbase,sp
Description: In GWmodel, we introduce 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, GW principal components analysis, GW discriminant analysis and various forms of GW regression; some of which are provided in basic and robust (outlier resistant) forms.
Packaged: 2015-02-01 14:16:43 UTC; Administrator

Depends: R (>= 3.0.0),maptools (>= 0.5-2), robustbase,sp,Rcpp
Packaged: 2016-11-30 11:25:44 UTC; Administrator

Packaged: 2017-03-16 04:52:43 UTC; Administrator

Packaged: 2017-03-28 06:00:34 UTC; Administrator

Packaged: 2017-05-21 06:35:17 UTC; Administrator

Depends: R (>= 3.0.0),maptools (>= 0.5-2), robustbase,sp,Rcpp, spdep
Packaged: 2017-12-20 02:04:50 UTC; Administrator

Packaged: 2018-08-20 07:31:48 UTC; Administrator

Packaged: 2019-02-01 14:56:10 UTC; Administrator

Packaged: 2019-02-13 15:27:29 UTC; Administrator

Depends: R (>= 3.0.0),maptools (>= 0.5-2), robustbase,sp,Rcpp,
Packaged: 2019-04-29 08:51:11 UTC; Administrator

Author: Binbin Lu[aut], Paul Harris[aut], Martin Charlton[aut], Chris Brunsdon[aut], Tomoki Nakaya[aut], Daisuke Murakami[aut],Isabella Gollini[ctb]

Packaged: 2019-07-08 10:11:56 UTC; Administrator

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.
Packaged: 2019-07-13 06:52:11 UTC; Administrator