Scalable Bayesian Disease Mapping Models for High-Dimensional Data R package bigDM version 0.5.7 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). Software 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 Aritz Adin <aritz.adin@unavarra.es> Comprehensive R Archive Network (CRAN) Aritz Adin [aut, cre] (ORCID: <https://orcid.org/0000-0003-3232-6147>), Erick Orozco-Acosta [aut] (ORCID: <https://orcid.org/0000-0002-1170-667X>), Maria Dolores Ugarte [aut] (ORCID: <https://orcid.org/0000-0002-3505-8400>) GPL-3 2025-09-16 application/tgz https://CRAN.R-project.org/package=bigDM doi:10.32614/CRAN.package.bigDM