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Scalable Bayesian disease mapping models (univariate and multivariate) for high-dimensional data using a divide and conquer approach.

Table of contents

The package

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.

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


Installing Rtools44 for Windows

R version 4.4.0 and newer for Windows requires the new Rtools44 to build R packages with C/C++/Fortran code from source.

Install from CRAN


Install from GitHub (development version)

# Install devtools package from CRAN repository

# Load devtools library

# Install the R-INLA package
install.packages("INLA", repos=c(getOption("repos"), INLA=""), dep=TRUE)

# In some Linux OS, it might be necessary to first install the following packages

# Install bigDM from GitHub repositoy

IMPORTANT NOTE: At least the stable version of INLA 22.11.22 (or newest) must be installed for the correct use of the bigDM package.

Basic Use

See the following vignettes for further details and examples using this package: * bigDM: fitting spatial models * bigDM: parallel and distributed modelling * bigDM: fitting spatio-temporal models * bigDM: fitting multivariate spatial models

When using this package, please cite the following papers:

Orozco-Acosta, E., Adin, A., and Ugarte, M.D. (2021). Scalable Bayesian modeling for smoothing disease risks in large spatial data sets using INLA. Spatial Statistics, 41, 100496.

Orozco-Acosta, E., Adin, A., and Ugarte, M.D. (2023). Big problems in spatio-temporal disease mapping: methods and software. Computer Methods and Programs in Biomedicine, 231, 107403.

Vicente, G., Adin, A., Goicoa, T., and Ugarte, M.D. (2023). High-dimensional order-free multivariate spatial disease mapping. Statistics and Computing, 33, 104.



Changes in version 0.5.4 (2024 May 30) * small bugs fixed and performance improvements * package built for R-4.4

Changes in version 0.5.3 (2023 Oct 17) * bugs fixed * faster implementation of divide_carto() function

Changes in version 0.5.2 (2023 Jun 14) * changes in mergeINLA() function * ‘X’ argument included to STCAR_INLA() function

Changes in version 0.5.1 (2023 Feb 14) * small bugs fixed * new inla.mode and num.threads arguments for CAR_INLA(), STCAR_INLA() and MCAR_INLA() functions * adaptation of STCAR_INLA() function for spatio-temporal predictions * parallelization improvements using future package

Changes in version 0.5.0 (2022 Oct 27) * new MCAR_INLA() function to fit scalable spatial multivariate CAR models * changes in mergeINLA() function * development of additional auxiliary functions

Changes in version 0.4.2 (2022 Jun 27) * small bugs fixed * new merging strategy

Changes in version 0.4.1 (2022 Feb 01) * small bugs fixed * version submmited to CRAN

Changes in version 0.4.0 (2022 Jan 21) * new STCAR_INLA() function to fit scalable spatio-temporal CAR models

Changes in version 0.3.2 (2021 Nov 05) * X and confounding arguments included to CAR_INLA() function * new function included: clustering_partition()

Changes in version 0.3.1 (2021 May 03) * W argument included to CAR_INLA() function

Changes in version 0.3.0 (2021 Apr 19) * parallel and distributed computation strategies when fitting inla models with the CAR_INLA() function

Changes in version 0.2.2 (2021 Mar 12) * new arguments included to random_partition() function

Changes in version 0.2.1 (2021 Feb 25) * Carto_SpainMUN data changed

Changes in version 0.2.0 (2020 Oct 01) * speedup improvements in mergeINLA() function * small bugs fixed


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).