Scalable Bayesian Disease Mapping Models for High-Dimensional Data
Add isolated areas (polygons) to its nearest neighbour
Compute correlation coefficients between diseases
Scalable Bayesian Disease Mapping Models for High-Dimensional Data
Fit a (scalable) spatial Poisson mixed model to areal count data, wher...
Obtain a partition of the spatial domain using the density-based spati...
Merge disjoint connected subgraphs
Divide the spatial domain into subregions
Fit a (scalable) spatial multivariate Poisson mixed model to areal cou...
Merge inla objects for partition models
Intrinsic multivariate CAR latent effect
Spatially non-structured multivariate latent effect
Leroux et al. (1999) multivariate CAR latent effect
Proper multivariate CAR latent effect
Define a random partition of the spatial domain based on a regular gri...
Fit a (scalable) spatio-temporal Poisson mixed model to areal count da...
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).
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