{
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  "Date": "2024-09-07",
  "Title": "Geographically-Weighted Models",
  "Description": "Techniques from a particular branch of spatial\nstatistics,termed geographically-weighted (GW) models. GW\nmodels suit situations when data are not described well by some\nglobal model, but where there are spatial regions where a\nsuitably localised calibration provides a better description.\n'GWmodel' includes functions to calibrate: GW summary\nstatistics (Brunsdon et al., 2002)<doi:\n10.1016/s0198-9715(01)00009-6>, GW principal components\nanalysis (Harris et al., 2011)<doi:\n10.1080/13658816.2011.554838>, GW discriminant analysis\n(Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x>\nand various forms of GW regression (Brunsdon et al., 1996)<doi:\n10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided\nin basic and robust (outlier resistant) forms.",
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  "Author": "Binbin Lu [aut, cre], Paul Harris [aut], Martin Charlton [aut],\nChris Brunsdon [aut], Tomoki Nakaya [aut], Daisuke Murakami\n[ctb], Yigong Hu [ctb], Fiona H Evans [ctb], Hjalmar\nH<c3><b6>glund [ctb]",
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      "title": "Geographically-Weighted Models",
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        "GWmodel"
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    {
      "page": "bw.ggwr",
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      "commit": "60dc1cee77957aa4f899ca1bd4898d151dc579af",
      "fileid": "d067111d8d9f387cc90171be01d04775d123368c3cbbd330204b2b719e919791",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/lbb220/actions/runs/27006328633"
    }
  ]
}