Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023 <doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023 <doi:10.1007/s11222-023-10263-x>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).
Version: 0.5.6 Depends: R (≥ 4.0.0) Imports: crayon, doParallel, fastDummies, foreach, future, future.apply, geos, MASS, Matrix, methods, parallel, parallelly, RColorBrewer, Rdpack, sf, spatialreg, spdep, stats, utils, rlist Suggests: bookdown, INLA (≥ 22.12.16), knitr, rmarkdown, testthat (≥ 3.0.0), tmap Published: 2025-03-25 DOI: 10.32614/CRAN.package.bigDM Author: Aritz Adin [aut, cre], Erick Orozco-Acosta [aut], Maria Dolores Ugarte [aut] Maintainer: Aritz Adin <aritz.adin at unavarra.es> BugReports: https://github.com/spatialstatisticsupna/bigDM/issues License: GPL-3 URL: https://github.com/spatialstatisticsupna/bigDM NeedsCompilation: no Additional_repositories: https://inla.r-inla-download.org/R/stable Citation: bigDM citation info Materials: README NEWS CRAN checks: bigDM results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=bigDM to link to this page.
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