Title: Scalable Bayesian Disease Mapping Models for High-Dimensional
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>).
Imports: crayon, future, future.apply, MASS, Matrix, methods, parallel,
RColorBrewer, Rdpack, sf, spatialreg, spdep, stats, utils,
Suggests: bookdown, INLA (>= 21.11.22), knitr, rmarkdown, testthat (>=
Author: Aritz Adin [aut, cre] (<https://orcid.org/0000-0003-3232-6147>),
Maria Dolores Ugarte [aut] (<https://orcid.org/0000-0002-3505-8400>)

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>). 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).

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., 2022 <arXiv:2201.08323> and Vicente et al., 2022 <arXiv:2210.14849>). 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).
Imports: crayon, fastDummies, future, future.apply, MASS, Matrix,
methods, parallel, RColorBrewer, Rdpack, sf, spatialreg, spdep,