Scalable Bayesian Disease Mapping Models for High-Dimensional Data

Documentation for package ‘bigDM’ version 0.4.1

bigDM-package	Scalable Bayesian Disease Mapping Models for High-Dimensional Data
add_neighbour	Add isolated areas (polygons) to its nearest neighbour
bigDM	Scalable Bayesian Disease Mapping Models for High-Dimensional Data
Carto_SpainMUN	Spanish colorectal cancer mortality data
CAR_INLA	Fit a (scalable) spatial Poisson mixed model to areal count data, where several CAR prior distributions can be specified for the spatial random effect.
clustering_partition	Obtain a partition of the spatial domain using the density-based spatial clustering (DBSC) algorithm described in Santafé et al. (2021)
connect_subgraphs	Merge disjoint connected subgraphs
Data_LungCancer	Spanish lung cancer mortality data
divide_carto	Divide the spatial domain into subregions
mergeINLA	Merge 'inla' objects for partition models
random_partition	Define a random partition of the spatial domain based on a regular grid
STCAR_INLA	Fit a (scalable) spatio-temporal Poisson mixed model to areal count data.