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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).
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Package details |
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| Author | Aritz Adin [aut, cre] (<https://orcid.org/0000-0003-3232-6147>), Erick Orozco-Acosta [aut] (<https://orcid.org/0000-0002-1170-667X>), Maria Dolores Ugarte [aut] (<https://orcid.org/0000-0002-3505-8400>) |
| Maintainer | Aritz Adin <aritz.adin@unavarra.es> |
| License | GPL-3 |
| Version | 0.5.5 |
| URL | https://github.com/spatialstatisticsupna/bigDM |
| Package repository | View on CRAN |
| Installation |
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