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 SantafĂ© 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. |