bigDM-package {bigDM}

Scalable Bayesian Disease Mapping Models for High-Dimensional Data

Description

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.

Details

Below, there is a list with a brief overview of all package functions:

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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Author(s)

Maintainer: Aritz Adin <[e-mail]>

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).

References

Orozco-Acosta E, Adin A, Ugarte MD (2021). “Scalable Bayesian modeling for smoothing disease mapping risks in large spatial data sets using INLA.” Spatial Statistics, 41, 100496. doi:10.1016/j.spasta.2021.100496.

Orozco-Acosta E, Adin A, Ugarte MD (2023). “Big problems in spatio-temporal disease mapping: methods and software.” Computer Methods and Programs in Biomedicine, 231, 107403. doi:10.1016/j.cmpb.2023.107403.

Vicente G, Adin A, Goicoa T, Ugarte MD (2023). “High-dimensional order-free multivariate spatial disease mapping.” Statistics and Computing, 33(5), 104. doi:10.1007/s11222-023-10263-x.

See Also

See the following vignettes for further details and examples using this package:

1. bigDM: fitting spatial models

2. bigDM: parallel and distributed modelling

3. bigDM: fitting spatio-temporal models

4. bigDM: fitting multivariate spatial models

Examples

## See the examples for CAR_INLA, MCAR_INLA and STCAR_INLA functions