commit source code

DESCRIPTION

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+Package: GWmodel
+Type: Package
+Version: 2.0-5
+Date: 2017-12-20
+Title: Geographically-Weighted Models
+Depends: R (>= 3.0.0),maptools (>= 0.5-2), robustbase,sp,Rcpp, spdep
+Imports: methods, grDevices, stats,graphics
+LinkingTo: Rcpp, RcppArmadillo
+Suggests: mvoutlier, RColorBrewer, gstat
+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.
+Author: Binbin Lu[aut], Paul Harris[aut], Martin Charlton[aut], Chris Brunsdon[aut], Tomoki Nakaya[aut], Isabella Gollini[ctb]
+Maintainer: Binbin Lu <[email protected]>
+License: GPL (>= 2)
+Repository: CRAN
+URL: http://gwr.nuim.ie/

NAMESPACE

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+useDynLib(GWmodel)
+import(Rcpp)
+import(maptools)
+import(robustbase)
+import(sp)
+import(spdep)
+import(stats)
+importFrom("grDevices", "grey", "rainbow", "rgb")
+importFrom("methods", "as", "is", "slot")
+importFrom("graphics", "abline", "axis", "hist", "identify", "legend",
+             "lines", "points", "rect", "text")
+export(gwr.bootstrap,generate.lm.data,parametric.bs,parametric.bs.local,se.bs,bias.bs,ci.bs,pval.bs,gwrtvar,gwrt.mlr,gwrt.lag,gwrt.err,gwrt.sma,bw.gwr3,gwr.psdm,gwr.backfit,bw.gwr2, confusion.matrix,gwss.montecarlo,gwda,print.gwda,grouping.xy,wqda,wlda,splitx,wmean,wvarcov,wprior,bw.gwda,wqda.cr,wlda.cr,gwr.hetero,gwr.mixed,gwr.mixed.2,gwr.mixed.trace,print.mgwr,gwr.q,gwr.collin.diagno,plot.mcsims,bw.gwr, gwr.cv,gwr.cv.contrib, gwr.aic, gold, gw.dist, gw.weight.box, gw.weight.gau, gw.weight.bis, gw.weight.tri, gw.weight.gau.ad, gw.weight.bis.ad, gw.weight.tri.ad, gw.weight.box.ad, gw.weight, gwr.basic, F1234.test,extract.mat,Generate.formula,gwr.generalised,gwr.poisson,gwr.binomial,gwss,bw.ggwr,ggwr.basic,ggwr.cv,ggwr.cv.contrib,ggwr.aic,gwr.poisson.wt,gwr.binomial.wt,wpca,robustSvd,wt.median,rwpca,gwpca,bw.gwpca,gwpca.cv,gwpca.cv.contrib,gwr.lcr,ridge.lm,bw.gwr.lcr,gwr.lcr.cv,gwr.lcr.cv.contrib,gwr.t.adjust,gwr.predict,gw.reg1,gwr.robust,gw.pcplot,bw.gwr1,gwr.aic1,gwr.cv1,plot.pvlas,coordinate_rotate,eu_dist_mat,eu_dist_smat,eu_dist_vec,mk_dist_mat,mk_dist_smat,mk_dist_vec,cd_dist_mat,cd_dist_smat,cd_dist_vec,md_dist_mat,md_dist_smat,md_dist_vec,bisq_wt_vec,bisq_wt_mat,gauss_wt_vec,gauss_wt_mat,tri_wt_vec,tri_wt_mat,exp_wt_vec,exp_wt_mat,gw_reg,gw.fitted,ehat,rss,gwr_diag,AICc,AICc_rss,Ci_mat,gwss.montecarlo,gwpca.montecarlo.1,gwpca.montecarlo.2,gwr.model.selection,gwr.model.view,gwr.model.sort,gwr.montecarlo,gwr.write,gwr.write.shp,gwpca.glyph.plot,gwpca.check.components,gwr.mink.approach,gwr.mink.matrixview,gwr.mink.pval,gwr.mink.pval.forward,gwr.mink.pval.backward,gw.mean.cv,gw.median.cv,gw.average.cv,gw.average.cv.contrib,bw.gwss.average,montecarlo.gwss,montecarlo.gwpca.1,montecarlo.gwpca.2,model.selection.gwr,model.view.gwr,model.sort.gwr,montecarlo.gwr,writeGWR,writeGWR.shp,glyph.plot,check.components,mink.approach,mink.matrixview)
+useDynLib(GWmodel, .registration = TRUE)
+
+S3method(print, mcsims)
+S3method(print, gwrm)
+S3method(print, ggwrm)
+S3method(print, gwss)
+S3method(print, gwrlcr)
+S3method(print, gwrm.pred)
+S3method(plot, mcsims)
+S3method(plot, pvlas)
+S3method(print, gwda)
+S3method(print, mgwr)
+S3method(print, gwrbsm)
+S3method(print, psdmgwr)