{ "_id": "6a1fe672b401979e7343c4d6", "Package": "mlspatial", "Title": "Machine Learning and Mapping for Spatial Epidemiology", "Version": "0.1.1", "Authors@R": "c(\nperson(\"Adeboye\", \"Azeez\", , \"azizadeboye@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Colin\", \"Noel\", role = \"aut\")\n)", "Description": "Provides tools for the integration, visualisation, and\nmodelling of spatial epidemiological data using the method\ndescribed in Azeez, A., & Noel, C. (2025). 'Predictive\nModelling and Spatial Distribution of Pancreatic Cancer in\nAfrica Using Machine Learning-Based Spatial Model'\n and\n. It facilitates the analysis of\ngeographic health data by combining modern spatial mapping\ntools with advanced machine learning (ML) algorithms.\n'mlspatial' enables users to import and pre-process shapefile\nand associated demographic or disease incidence data, generate\nrichly annotated thematic maps, and apply predictive models,\nincluding Random Forest, 'XGBoost', and Support Vector\nRegression, to identify spatial patterns and risk factors. It\nis suited for spatial epidemiologists, public health\nresearchers, and GIS analysts aiming to uncover hidden\ngeographic patterns in health-related outcomes and inform\nevidence-based interventions.", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "Config/testthat/edition": "3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-03 08:25:14 UTC", "User": "root" }, "Author": "Adeboye Azeez [aut, cre], Colin Noel [aut]", "Maintainer": "Adeboye Azeez ", "Config/pak/sysreqs": "libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev\nmake libicu-dev libpng-dev libuv1-dev libxml2-dev libssl-dev\nlibproj-dev libsqlite3-dev libudunits2-dev zlib1g-dev", "Repository": "https://azizadeboye.r-universe.dev", "Date/Publication": "2026-03-30 06:33:46 UTC", "RemoteUrl": "https://github.com/azizadeboye/mlspatial", "RemoteRef": "HEAD", "RemoteSha": "28b84a17ce6170ae7360bc8e757d944aafcb0446", "MD5sum": 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