{ "_id": "6a2a847b7b7a29ca600487de", "Package": "disaggregation", "Type": "Package", "Title": "Disaggregation Modelling", "Version": "0.4.0", "Authors@R": "c(\nperson(\"Anita\", \"Nandi\", email = \"anita.k.nandi@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-5087-2494\")),\nperson(\"Tim\", \"Lucas\", email = \"timcdlucas@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4694-8107\")),\nperson(\"Rohan\", \"Arambepola\", email = \"rarambepola@gmail.com\", role = \"aut\"),\nperson(\"Andre\", \"Python\", email = \"python.andre@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0001-8094-7226\")),\nperson(\"Simon\", \"Smart\", email = \"simon.smart@cantab.net\", role = \"ctb\")\n)", "Description": "Fits disaggregation regression models using 'TMB'\n('Template Model Builder'). When the response data are\naggregated to polygon level but the predictor variables are at\na higher resolution, these models can be useful. Regression\nmodels with spatial random fields. The package is described in\ndetail in Nandi et al. 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