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  "Title": "A Hybrid Spatial Model for Capturing Spatially Varying\nRelationships Between Variables in the Data",
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  "Authors@R": "c(person(\"Nobin Chandra\",\"Paul\", role=c(\"aut\",\"cre\",\"cph\"), email=\"nobin.paul@icar.gov.in\"),person(\"Anil\",\"Rai\",role=\"aut\"),person(\"Ankur\",\"Biswas\",role=\"aut\"),person(\"Tauqueer\",\"Ahmad\",role=\"aut\"),person(\"Dhananjay D.\",\"Nangare\",role=\"aut\"),person(\"Bhaskar B.\",\" Gaikwad\",role=\"aut\"))",
  "Description": "It is a hybrid spatial model that combines the strength of\ntwo widely used regression models, MARS (Multivariate Adaptive\nRegression Splines) and GWR (Geographically Weighted\nRegression) to provide an effective approach for predicting a\nresponse variable at unknown locations. The MARS model is used\nin the first step of the development of a hybrid model to\nidentify the most important predictor variables that assist in\npredicting the response variable. For method details see,\nFriedman, J.H. (1991). <DOI:10.1214/aos/1176347963>.The GWR\nmodel is then used to predict the response variable at testing\nlocations based on these selected variables that account for\nspatial variations in the relationships between the variables.\nThis hybrid model can improve the accuracy of the predictions\ncompared to using an individual model alone.This developed\nhybrid spatial model can be useful particularly in cases where\nthe relationship between the response variable and predictor\nvariables is complex and non-linear, and varies across\nlocations.",
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  "Author": "Nobin Chandra Paul [aut, cre, cph], Anil Rai [aut], Ankur\nBiswas [aut], Tauqueer Ahmad [aut], Dhananjay D. Nangare [aut],\nBhaskar B.  Gaikwad [aut]",
  "Maintainer": "Nobin Chandra Paul <nobin.paul@icar.gov.in>",
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