# Scalable Bayesian Disease Mapping Models for High-Dimensional Data

## Documentation for package ‘bigDM’ version 0.5.6

- 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
Data_MultiCancer | Spanish cancer mortality data for the joint analysis of multiple diseases
divide_carto | Divide the spatial domain into subregions
MCAR_INLA | Fit a (scalable) spatial multivariate Poisson mixed model to areal count data where dependence between spatial patterns of the diseases is addressed through the use of M-models (Botella-Rocamora et al. 2015).
mergeINLA | Merge 'inla' objects for partition models
Mmodel_compute_cor | Compute correlation coefficients between diseases
Mmodel_icar | Intrinsic multivariate CAR latent effect
Mmodel_iid | Spatially non-structured multivariate latent effect
Mmodel_lcar | Leroux et al. (1999) multivariate CAR latent effect
Mmodel_pcar | Proper multivariate CAR latent effect
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.