{ "_id": "6a102fc1acfb0bcc41c950ed", "Package": "spatialRF", "Title": "Easy Spatial Modeling with Random Forest", "Version": "1.1.5", "Authors@R": "person(given = \"Blas M.\",\nfamily = \"Benito\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"blasbenito@gmail.com\",\ncomment = c(ORCID = \"0000-0001-5105-7232\"))", "URL": "https://blasbenito.github.io/spatialRF/", "BugReports": "https://github.com/BlasBenito/spatialRF/issues/", "Description": "Automatic generation and selection of spatial predictors\nfor Random Forest models fitted to spatially structured data.\nSpatial predictors are constructed from a distance matrix among\ntraining samples using Moran's Eigenvector Maps (MEMs; Dray,\nLegendre, and Peres-Neto 2006\n) or the RFsp approach\n(Hengl et al. ). These predictors are\nused alongside user-supplied explanatory variables in Random\nForest models. The package provides functions for model\nfitting, multicollinearity reduction, interaction\nidentification, hyperparameter tuning, evaluation via spatial\ncross-validation, and result visualization using partial\ndependence and interaction plots. Model fitting relies on the\n'ranger' package (Wright and Ziegler 2017\n).", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "Roxygen": "list(markdown = TRUE, old_usage = FALSE)", "RoxygenNote": "7.3.3", "Language": "en-US", "Config/pak/sysreqs": "texlive libicu-dev libxml2-dev", "Repository": "https://blasbenito.r-universe.dev", "Date/Publication": "2026-05-12 10:36:32 UTC", "RemoteUrl": "https://github.com/blasbenito/spatialrf", "RemoteRef": "HEAD", "RemoteSha": "d866cce67b91b54c3e6a8cfc9e09c58f1e6993f2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-12 11:22:52 UTC", "User": "root" }, "Author": "Blas M. Benito [aut, cre, cph] (ORCID:\n)", "Maintainer": "Blas M. 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