Bayesian Inference for Multivariate Spatial Models with R-INLA

Francisco Palmí-Perales, Virgilio Gómez-Rubio, Roger S Bivand, Michela Cameletti, Haavard Rue

Research output: Contribution to journalArticlepeer-review


Bayesian methods and software for spatial data analysis are generally well established now in the scientific community. Despite the wide application of spatial models, the analysis of multivariate spatial data using the integrated nested Laplace approximation through its R package (R-INLA) has not been widely described in the existing literature. Therefore, the main objective of this article is to demonstrate that R-INLA is a convenient toolbox to analyse different types of multivariate spatial datasets. This will be illustrated by analysing three datasets which are publicly available. Furthermore, the details and the R code of these analyses are provided to exemplify how to fit models to multivariate spatial datasets with R-INLA.
Original languageEnglish (US)
JournalAccepted by The R Journal
StatePublished - Sep 6 2023

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