Package: GWmodel 2.4-1
GWmodel: Geographically-Weighted Models
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.
Authors:
GWmodel_2.4-1.tar.gz
GWmodel_2.4-1.tar.gz(r-4.5-noble)GWmodel_2.4-1.tar.gz(r-4.4-noble)
GWmodel_2.4-1.tgz(r-4.4-emscripten)GWmodel_2.4-1.tgz(r-4.3-emscripten)
GWmodel.pdf |GWmodel.html✨
GWmodel/json (API)
# Install 'GWmodel' in R: |
install.packages('GWmodel', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- Dub.voter - Voter turnout data in Greater Dublin
- Gedu.counties - Georgia counties data
- Gedu.df - Georgia census data set
- USelect2004 - Results of the 2004 US presidential election at the county level
- ewhp - House price data set (DataFrame) in England and Wales
- ewoutline - Outline of England and Wales for data EWHP
- londonborough - London boroughs data
- londonhp - London house price data set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 months agofrom:60dc1cee77. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 07 2024 |
R-4.5-linux-x86_64 | OK | Dec 07 2024 |
Exports:AICcAICc_rssAICc_rss1AICc1bias.bsbw.ggwrbw.gtwrbw.gwdabw.gwpcabw.gwrbw.gwr.lcrbw.gwr1bw.gwr3bw.gwss.averagecheck.componentsCi_matci.bsconfusion.matrixe_vecehatextract.matF1234.testGenerate.formulagenerate.lm.dataggwr.aicggwr.basicggwr.cvggwr.cv.contribglyph.plotgoldgrouping.xygtwrgtwr.aicgw_BICgw_cv_allgw_cv_all_cudagw_cv_all_ompgw_distgw_fittedgw_local_r2gw_reggw_reg_1gw_reg_2gw_reg_allgw_reg_all_cudagw_reg_all_ompgw_weightgw_weight_matgw_weight_vecgw.average.cvgw.average.cv.contribgw.distgw.fittedgw.mean.cvgw.median.cvgw.pcplotgw.weightgwdagwpcagwpca.check.componentsgwpca.cvgwpca.cv.contribgwpca.glyph.plotgwpca.montecarlo.1gwpca.montecarlo.2gwr_diaggwr_diag1gwr_mixed_2gwr_mixed_tracegwr_qgwr.aicgwr.aic1gwr.backfitgwr.basicgwr.binomialgwr.binomial.wtgwr.bootstrapgwr.collin.diagnogwr.cvgwr.cv.contribgwr.cv1gwr.generalisedgwr.heterogwr.lcrgwr.lcr.cvgwr.lcr.cv.contribgwr.mink.approachgwr.mink.matrixviewgwr.mink.pvalgwr.mink.pval.backwardgwr.mink.pval.forwardgwr.mixedgwr.mixed.2gwr.mixed.tracegwr.mixed.trace.fastgwr.model.selectiongwr.model.sortgwr.model.viewgwr.montecarlogwr.multiscalegwr.poissongwr.poisson.wtgwr.predictgwr.qgwr.q2gwr.robustgwr.scalablegwr.scalable.loocvgwr.t.adjustgwr.writegwr.write.shpgwrt.errgwrt.laggwrt.mlrgwrt.smagwrtvargwssgwss.montecarlomink.approachmink.matrixviewmodel.selection.gwrmodel.sort.gwrmodel.view.gwrmontecarlo.gwpca.1montecarlo.gwpca.2montecarlo.gwrmontecarlo.gwssnew_multiscaleparametric.bsparametric.bs.localplot.mcsimsplot.pvlasprint.gwdaprint.mgwrpval.bsridge.lmrobustSvdrssrwpcascgwr_loocvscgwr_prescgwr_regse.bssplitxst.distti.distti.distmti.distvtrhat2wldawlda.crwmeanwpcawpriorwqdawqda.crwriteGWRwriteGWR.shpwt.medianwvarcov
Dependencies:bootclassclassIntcodacodetoolsDBIdeldirDEoptimRe1071FNNintervalsKernSmoothlatticeLearnBayesmagrittrMASSMatrixmultcompmvtnormnlmeproxyRcppRcppArmadilloRcppEigenrobustbases2sandwichsfspspacetimespatialregspDataspdepsurvivalTH.dataunitswkxtszoo