Geographically-Weighted Models
Bandwidth selection for generalised geographically weighted regression...
Bandwidth selection for GTWR
Bandwidth selection for GW Discriminant Analysis
Bandwidth selection for Geographically Weighted Principal Components A...
Bandwidth selection for locally compensated ridge GWR (GWR-LCR)
Bandwidth selection for basic GWR
Bandwidth selection for GW summary averages
Voter turnout data in Greater Dublin(SpatialPolygonsDataFrame)
Outline of England and Wales for data EWHP
Georgia counties data (SpatialPolygonsDataFrame)
Generalised GWR models with Poisson and Binomial options
Cross-validation data at each observation location for a generalised G...
Cross-validation score for a specified bandwidth for generalised GWR
Geographically and Temporally Weighted Regression
Distance matrix calculation
Geographically weighted parallel coordinate plot for investigating mul...
Weight matrix calculation
GW Discriminant Analysis
Geographically-Weighted Models
Interaction tool with the GWPCA glyph map
Cross-validation data at each observation location for a GWPCA
Cross-validation score for a specified bandwidth for GWPCA
Multivariate glyph plots of GWPCA loadings
Monte Carlo (randomisation) test for significance of GWPCA eigenvalue ...
Monte Carlo (randomisation) test for significance of GWPCA eigenvalue ...
GWPCA
Basic GWR model
Bootstrap GWR
Local collinearity diagnostics for basic GWR
Cross-validation data at each observation location for a basic GWR mod...
Cross-validation score for a specified bandwidth for basic GWR
Heteroskedastic GWR
Cross-validation data at each observation location for the GWR-LCR mod...
Cross-validation score for a specified bandwidth for GWR-LCR model
GWR with a locally-compensated ridge term
Minkovski approach for GWR
Visualisation of the results from gwr.mink.approach
Select the values of p for the Minkowski approach for GWR
Mixed GWR
Model selection for GWR with a given set of independent variables
Sort the results of the GWR model selection function `gwr.model.select...
Visualise the GWR models from gwr.model.selection
Monte Carlo (randomisation) test for significance of GWR parameter var...
Multiscale GWR
GWR used as a spatial predictor
Robust GWR model
Scalable GWR
Adjust p-values for multiple hypothesis tests in basic GWR
Write the GWR results into files
Monte Carlo (randomisation) test for gwss
Geographically weighted summary statistics (GWSS)
London boroughs data
Spatio-temporal distance matrix calculation
Results of the 2004 US presidential election at the county level (Spat...
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.