bigDM-package

bigDM

This package implements several (scalable) spatial and spatio-temporal Poisson mixed models for high-dimensional areal count data
in a fully Bayesian setting using the integrated nested Laplace approximation (INLA) technique.

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).

Aritz Adin

Scalable Bayesian Disease Mapping Models for High-Dimensional
Data

Erick Orozco-Acosta

Maria Dolores Ugarte

bigDM-package function

Maintainer: Aritz Adin &lt;aritz.adin@unavarra.es&gt;
 
This work 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) and by la Caixa Foundation (ID 1000010434), Caja Navarra Foundation and UNED Pamplona, under agreement LCF/PR/PR15/51100007 (project REF P/13/20).

Author

Scalable Bayesian Disease Mapping Models for High-Dimensional Data — bigDM-package

Maintainer: Aritz Adin &lt;aritz.adin@unavarra.es&gt;
 
This work 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) and by la Caixa Foundation (ID 1000010434), Caja Navarra Foundation and UNED Pamplona, under agreement LCF/PR/PR15/51100007 (project REF P/13/20).

`add_neighbour`	Adds isolated areas (polygons) to its nearest neighbour
`CAR_INLA`	Fits several spatial CAR models for high-dimensional count data
`clustering_partition`	Obtain a spatial partition using the DBSC algorithm
`connect_subgraphs`	Merges disjoint connected subgraphs
`divide_carto`	Divides the spatial domain into subregions
`MCAR_INLA`	Fits several spatial multivariate CAR models for high-dimensional count data
`mergeINLA`	Merges `inla` objects for partition models
`Mmodel_compute_cor`	Computes between-diseases correlation coefficients for M-models
`Mmodel_icar`	Implements the spatially non-structured multivariate latent effect
`Mmodel_icar`	Implements the intrinsic multivariate CAR latent effect
`Mmodel_lcar`	Implements the Leroux et al. (1999) multivariate CAR latent effect
`Mmodel_pcar`	Implements the proper multivariate CAR latent effect
`random_partition`	Defines a random partition of the spatial domain based on a regular grid
`STCAR_INLA`	Fits several spatio-temporal CAR models for high-dimensional count data
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bigDM-package: Scalable Bayesian Disease Mapping Models for High-Dimensional Data

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