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bigDM  

Scalable Bayesian Disease Mapping Models for High-Dimensional Data
View on CRAN: Click here


Download and install bigDM package within the R console
Install from CRAN:
install.packages("bigDM")

Install from Github:
library("remotes")
install_github("cran/bigDM")

Install by package version:
library("remotes")
install_version("bigDM", "0.5.7")



Attach the package and use:
library("bigDM")
Maintained by
Aritz Adin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-02-08
Latest Update: 2025-03-25
Description:
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 ; Orozco-Acosta et al., 2023 and Vicente et al., 2023 ). 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).
How to cite:
Aritz Adin (2022). bigDM: Scalable Bayesian Disease Mapping Models for High-Dimensional Data. R package version 0.5.7, https://cran.r-project.org/web/packages/bigDM. Accessed 12 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:21), 0.4.1 (2022-02-08 16:40), 0.4.2 (2022-06-27 11:10), 0.5.0 (2022-10-28 13:47), 0.5.1 (2023-02-22 10:00), 0.5.2 (2023-07-20 10:00), 0.5.3 (2023-10-17 14:50), 0.5.4 (2024-05-30 17:20), 0.5.5 (2024-08-19 14:00), 0.5.6 (2025-03-25 10:30)
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Complete documentation for bigDM
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