bigDM: Scalable Bayesian Disease Mapping Models for High-Dimensional Data
Aritz Adín, Erick Orozco‐Acosta, María Dolores Ugarte · 2022
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 ; Orozco-Acosta et al., 2023 and Vicente et al., 2023 ). 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).