Package: bigDM
Type: Package
Title: Scalable Bayesian Disease Mapping Models for High-Dimensional
        Data
Version: 0.4.1
Date: 2022-02-03
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> and Orozco-Acosta et al., 2022 <arXiv:2201.08323>).
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, 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
RdMacros: Rdpack
License: GPL-3
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.1.2
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2022-02-07 12:19:52 UTC; aritz.adin
Author: Aritz Adin [aut, cre] (<https://orcid.org/0000-0003-3232-6147>),
  Erick Orozco-Acosta [aut] (<https://orcid.org/0000-0002-1170-667X>),
  Maria Dolores Ugarte [aut] (<https://orcid.org/0000-0002-3505-8400>)
Repository: CRAN
Date/Publication: 2022-02-08 15:40:10 UTC
Built: R 4.0.5; ; 2022-02-09 12:03:02 UTC; unix
