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: | 0.5.7 |
| 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: | 2025-09-16 |
| 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 |