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> and Orozco-Acosta et al., 2022 <arXiv:2201.08323>). 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: |
0.4.2 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
crayon, future, future.apply, MASS, Matrix, methods, parallel, RColorBrewer, Rdpack, sf, spatialreg, spdep, stats, utils, rlist |
| Suggests: |
bookdown, INLA (≥ 21.11.22), knitr, rmarkdown, testthat (≥
3.0.0), tmap |
| Published: |
2022-06-27 |
| 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 |