# Scalable Bayesian Disease Mapping Models for High-Dimensional Data

## Documentation for package ‘bigDM’ version 0.4.1

- DESCRIPTION file.
- Package NEWS.

## Help Pages

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.