Scalable Bayesian disease mapping models for high-dimensional data using a divide and conquer approach.
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:
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-disease correlation
coefficients for M-models.Mmodel_idd Implements the spatially non-structured
multivariate latent effect.Mmodel_icar Implements the intrinsic multivariate
latent effect.Mmodel_lcar Implements the Leroux et al. (1999)
multivariate latent effect.Mmodel_pcar Implements the proper multivariate 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.Installing Rtools42 for Windows
R version 4.2.0 and newer for Windows requires the new Rtools42 to build R packages with C/C++/Fortran code from source.
install.packages("bigDM")# Install devtools package from CRAN repository
install.packages("devtools")
# Load devtools library
library(devtools)
# Install the R-INLA package
install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)
# In some Linux OS, it might be necessary to first install the following packages
install.packages(c("cpp11","proxy","progress","tzdb","vroom"))
# Install bigDM from GitHub repositoy
install_github("spatialstatisticsupna/bigDM")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.
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:
news(package="bigDM")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).
