R-INLA is a package in R that do approximate Bayesian inference for Latent Gaussian Models. This site is dedicated to that package and methodological developments that goes along with it.

What is INLA? The integrated nested Laplace approximation (INLA) is a method for approximate Bayesian inference. In the last years it has established itself as an alternative to other methods such …

Download & Install To install the INLA-package in R, you have to manually add the r-inla repository as we are not on CRAN. The source code for this project is hosted on github

INLA course on discrete data (Video 1: Intro - Video 2: Hands-on), Luigi &Thomas, at Spatial Data Science conference organized by the University of Lausanne. A gentle INLA tutorial - Precision …

Documentation Help Selected List of Latent Models, Likelihoods, Priors and Vignettes found in INLA: Selected Latent Models with Example on each: Selected Priors with Example on each: Selected …

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INLA seems to work great for near all cases, but are there cases where INLA is known to have problems? The methodology needs the full conditional density for the latent field to be "near" Gaussian.

Online version is available here. It provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial …

Spatial and Spatio-Temporal models with R-INLA , Blangiardo, Cameletti, Baio, and Rue , 2012. The data-sets are found here and the R-code is found here , for the first two examples. For examples …

A detailed (?) description of each function in the R-INLA package can be obtained by typing in R any of the two commands below: You can also have a look at our FAQ page. If you have questions about …