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).

Version:	0.5.3
Depends:	R (≥ 4.0.0)
Imports:	crayon, doParallel, fastDummies, foreach, future, future.apply, geos, MASS, Matrix, methods, parallel, RColorBrewer, Rdpack, sf, spatialreg, spdep, stats, utils, rlist
Suggests:	bookdown, INLA (≥ 22.12.16), knitr, rmarkdown, testthat (≥ 3.0.0), tmap
Published:	2023-10-17
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

Documentation:

Reference manual:

bigDM.pdf

Downloads:

Package source:	bigDM_0.5.3.tar.gz
Windows binaries:	r-devel: bigDM_0.5.3.zip, r-release: bigDM_0.5.3.zip, r-oldrel: bigDM_0.5.3.zip
macOS binaries:	r-release (arm64): bigDM_0.5.3.tgz, r-oldrel (arm64): bigDM_0.5.3.tgz, r-release (x86_64): bigDM_0.5.3.tgz, r-oldrel (x86_64): bigDM_0.5.3.tgz
Old sources:	bigDM archive

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