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> and Orozco-Acosta et al., 2022 <arXiv:2201.08323>).

Version: 0.4.1
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: 2022-02-08
Author: Aritz Adin ORCID iD [aut, cre], Erick Orozco-Acosta ORCID iD [aut], Maria Dolores Ugarte ORCID iD [aut]
Maintainer: Aritz Adin <aritz.adin at>
License: GPL-3
NeedsCompilation: no
Citation: bigDM citation info
Materials: README NEWS
CRAN checks: bigDM results


Reference manual: bigDM.pdf


Package source: bigDM_0.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bigDM_0.4.1.tgz, r-release (x86_64): bigDM_0.4.1.tgz, r-oldrel: bigDM_0.4.1.tgz


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