Scalable Bayesian Disease Mapping Models for High-Dimensional Data

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Documentation for package ‘bigDM’ version 0.5.1

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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.