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.


The R-INLA Workshop was a Big Success: An international virtual workshop entitled Introducing R-INLA and its Applications was held by Statistics Study Program on Wednesday, September 30th, 2020 at Airlangga University, Indonesia. Click here.

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An INLA-translation of the 2019 homework of Statistical Rethinking: (2nd edition), 23 Sep 2020


Impressive, well done!


New version of the R-INLA package

A new version of the R-INLA package, 21.06.11, is out with some nice new features.

  • Native build for R-4.1 and Mac M1 processor, included ported PARDISO library.

  • For non-Mac-M1, no R-4.1 support yet, we're still building on R-4.0.

  • Updated PARDISO library to version 7.2 on (Intel based) Mac.

  • The largest change, is a preview of a new option that with 'twostage=TRUE' enables an internal reformulation of the model that will run faster for 'data-rich' models. It is default not enabled. This feature is highly experimental and will likely break in some cases. If it does, please send a reproducible example so it can be fixed.


Solving Real-World Problems: A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks, 2018.

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