{ "_id": "6ac60583f511ae58c09a4c51", "Package": "bigDM", "Type": "Package", "Title": "Scalable Bayesian Disease Mapping Models for High-Dimensional\nData", "Version": "0.5.9", "Date": "2026-10-06", "Authors@R": "c(person(given = \"Aritz\",\nfamily = \"Adin\",\nrole = c(\"aut\", \"cre\"),\nemail = \"aritz.adin@unavarra.es\",\ncomment = c(ORCID = \"0000-0003-3232-6147\")),\nperson(given = \"Erick\",\nfamily = \"Orozco-Acosta\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-1170-667X\")),\nperson(given = \"Maria Dolores\",\nfamily = \"Ugarte\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-3505-8400\"))\n)", "Maintainer": "Aritz Adin ", "Description": "Implements several spatial and spatio-temporal scalable\ndisease mapping models for high-dimensional count data using\nthe INLA technique for approximate Bayesian inference in latent\nGaussian models (Orozco-Acosta et al., 2021\n; Orozco-Acosta et al., 2023\n and Vicente et al., 2023\n). 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It has\nalso been partially funded by the Public University of Navarra\n(project PJUPNA2001).", "URL": "https://github.com/spatialstatisticsupna/bigDM", "BugReports": "https://github.com/spatialstatisticsupna/bigDM/issues", "Additional_repositories": "https://inla.r-inla-download.org/R/stable", "RdMacros": "Rdpack", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev\nlibicu-dev libxml2-dev libssl-dev libproj-dev libsqlite3-dev\nlibudunits2-dev", "Repository": "https://spatialstatisticsupna.r-universe.dev", "Date/Publication": "2026-10-06 11:20:57 UTC", "RemoteUrl": "https://github.com/spatialstatisticsupna/bigdm", "RemoteRef": "HEAD", "RemoteSha": "253e2a01288b9e5f9c3b52e8eead0c313a0abf0d", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-07 08:22:43 UTC", "User": "root" }, "Author": "Aritz Adin 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