bigDM: Scalable Bayesian Disease Mapping Models for High-Dimensional Data

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: 0.5.4
Depends: R (≥ 4.0.0)
Imports: crayon, doParallel, fastDummies, foreach, future, future.apply, geos, MASS, Matrix, methods, parallel, RColorBrewer, Rdpack, sf, spatialreg, spdep, stats, utils, rlist
Suggests: bookdown, INLA (≥ 22.12.16), knitr, rmarkdown, testthat (≥ 3.0.0), tmap
Published: 2024-05-30
DOI: 10.32614/CRAN.package.bigDM
Author: Aritz Adin ORCID iD [aut, cre], Erick Orozco-Acosta ORCID iD [aut], Maria Dolores Ugarte ORCID iD [aut]
Maintainer: Aritz Adin <aritz.adin at>
License: GPL-3
NeedsCompilation: no
Citation: bigDM citation info
Materials: README NEWS
CRAN checks: bigDM results


Reference manual: bigDM.pdf


Package source: bigDM_0.5.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bigDM_0.5.4.tgz, r-oldrel (arm64): bigDM_0.5.4.tgz, r-release (x86_64): bigDM_0.5.4.tgz, r-oldrel (x86_64): bigDM_0.5.4.tgz
Old sources: bigDM archive


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