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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 |
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 |
Reference manual: | bigDM.pdf |
Package source: | bigDM_0.5.4.tar.gz |
Windows binaries: | r-devel: bigDM_0.5.4.zip, r-release: bigDM_0.5.4.zip, r-oldrel: bigDM_0.5.4.zip |
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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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