DESCRIPTION

Package: bigDM
Type: Package
Title: Scalable Bayesian Disease Mapping Models for High-Dimensional Data
Version: 0.5.5
Date: 2024-08-19
Authors@R: 
  c(person(given = "Aritz",
           family = "Adin",
           role = c("aut", "cre"),
           email = "aritz.adin@unavarra.es",
           comment = c(ORCID = "0000-0003-3232-6147")),
    person(given = "Erick",
           family = "Orozco-Acosta",
           role = "aut",
           comment = c(ORCID = "0000-0002-1170-667X")),
    person(given = "Maria Dolores",
           family = "Ugarte",
           role = "aut",
           comment = c(ORCID = "0000-0002-3505-8400"))
    )
Maintainer: Aritz Adin <aritz.adin@unavarra.es>
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 <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).
URL: https://github.com/spatialstatisticsupna/bigDM
BugReports: https://github.com/spatialstatisticsupna/bigDM/issues
Depends: R (>= 4.0.0)
Additional_repositories: https://inla.r-inla-download.org/R/stable
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
RdMacros: Rdpack
License: GPL-3
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.3.2
Config/testthat/edition: 3