bigDM-package | R Documentation |
This package implements several (scalable) spatial and spatio-temporal Poisson mixed models for high-dimensional areal count data in a fully Bayesian setting using the integrated nested Laplace approximation (INLA) technique.
Below, there is a list with a brief overview of all package functions:
add_neighbour | Adds isolated areas (polygons) to its nearest neighbour |
CAR_INLA | Fits several spatial CAR models for high-dimensional count data |
clustering_partition | Obtain a spatial partition using the DBSC algorithm |
connect_subgraphs | Merges disjoint connected subgraphs |
divide_carto | Divides the spatial domain into subregions |
MCAR_INLA | Fits several spatial multivariate CAR models for high-dimensional count data |
mergeINLA | Merges inla objects for partition models |
Mmodel_compute_cor | Computes between-diseases correlation coefficients for M-models |
Mmodel_icar | Implements the spatially non-structured multivariate latent effect |
Mmodel_icar | Implements the intrinsic multivariate CAR latent effect |
Mmodel_lcar | Implements the Leroux et al. (1999) multivariate CAR latent effect |
Mmodel_pcar | Implements the proper multivariate CAR latent effect |
random_partition | Defines a random partition of the spatial domain based on a regular grid |
STCAR_INLA | Fits several spatio-temporal CAR models for high-dimensional count data |
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Maintainer: Aritz Adin <aritz.adin@unavarra.es>
This work has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033.
It has also been partially funded by the Public University of Navarra (project PJUPNA2001) and by la Caixa Foundation (ID 1000010434), Caja Navarra Foundation and UNED Pamplona, under agreement LCF/PR/PR15/51100007 (project REF P/13/20).
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See the following vignettes for further details and examples using this package:
## See the examples for CAR_INLA, MCAR_INLA and STCAR_INLA functions ##
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