Spatial and Spatio-temporal Bayesian Models with R - INLA

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Spatial modelling with R-INLA: A review (2018, methodology): “…not give a detailed introduction of R-INLA, which is found in Rue et al. (2017), or how to program spatial models in R-INLA, which is found in Lindgren and Rue (2015); Blangiardo and Cameletti (2015); Bivand et al. (2015); G´omez-Rubio et al. (2014); Krainski et al. (2017) and at www.r-inla.org .”
Space-time landslide predictive modelling (2019, methodology): “…into global multiple intercepts (with one coefficient for each time interval), assumed to be a priori independent across time, and latent temporal effects (LTEs) for each SU, assumed to be driven by an autoregressive temporal dependence structure (Blangiardo and Cameletti, 2015; Opitz, 2017).”
Bayesian Computing with INLA: A Review (2016, background): “Documentation is something we could and should improve upon, but the recent book by Blangiardo and Cameletti (2015) does a really good job.”
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, background): “Section 6 summarizes results 42 1 See Rue et al. (2009) for details on the INLA approach and Blangiardo and Cameletti (2015) for its R programming.”