News

R version 3.5

posted 12 Jul 2018, 04:27 by Havard Rue

We have moved to R-3.5. There is a testing/stable version of INLA Version_18.07.11  that is built with 3.4, and essentially the same testing/stable version is also available as Version_18.07.12 built with R-3.5.  

You have to reinstall all packages when moving from R-3.4 to R-3.5, due to an internal change in the binary format. 

Please let us know if there are any problems with INLA & R-3.5.

H

Upcoming INLA-related tutorials at useR! and RaukR

posted 11 Jun 2018, 01:22 by Havard Rue

Paula Moraga from Lancaster Medical school, will give two INLA related tutorials at the upcoming useR! meeting in Brisbane and the RaukR summer school in Visby, Sweden. The link to the tutorials (and other useful stuff!) is here

H



New web address for shiny application SSTCDapp

posted 27 May 2018, 13:37 by Andrea Riebler

The shiny application SSTCDapp developed by the spatial statistics group at the Public University of Navarre moved to a new location




SSTCDapp has been designed for the following purposes:

  • To perform descriptive analyses in space and time of mortality/incidence risks or rates.
  • To fit a wide variety of spatial and spatio-temporal hierarchical models commonly used in disease mapping.

The SSTCDapp facilitates the use of these models, since some of them are difficult to implement for non-expert users working in epidemiology or public health institutes. The application may also be used for the analysis of similar problems in many other fields such as criminology, gender-based violence, road traffic accidents or veterinary epidemiology.

The application allows the users to upload their data and its associated cartography, to generate many graphs and tables for the descriptive analysis of mortality/incidence of risks/rates, and last but not least, to fit fairly complex spatio-temporal models to smooth risks in space and time. The recently proposed integrated nested Laplace approximation (INLA) technique for Bayesian inference is used for model fitting and inference using the R-INLA package.

How besag/bym and those models should be defined...

posted 23 May 2018, 12:07 by Havard Rue   [ updated 23 May 2018, 12:17 ]

The besag/bym model has been around for quite some time, and is extensively used.  When applied to disconnected graphs, the current practice has varied, as the original formulation by BYM, implicitly assumed a connected graph.  Anna.FS & Massimo.V  have now defined this in general for a disconnected graph, including how to do proper scaling of the model so that priors make sense. This is how besag/bym/etc with scale.model=TRUE is implemented in R-INLA. The link to the paper is here, and is free before 12th July 2018. Otherwise, you'll find older versions on arxiv.

H



Upcoming INLA course in Zurich in October

posted 21 May 2018, 23:56 by Haakon Bakka   [ updated 21 May 2018, 23:56 ]

Point patterns help to predict landslides

posted 20 May 2018, 00:20 by Havard Rue   [ updated 21 May 2018, 23:58 by Haakon Bakka ]

Very nice work by colleagues here at KAUST... See here for an ad with video




GEOMED2019

posted 17 May 2018, 06:48 by Havard Rue

The GEOMED2019 announcement just came out and the meeting will take place in Glasgow end of August 2019. It is a pleasure to see that Martha & Michela is giving a pre-conference workshop:  "Spatial and spatio-temporal Bayesian Models with R-INLA"  (based on their book with the same name). 

H


Fitting geostatistical and spatial point process models to spatial survey data

posted 4 May 2018, 10:17 by Havard Rue

Course in Lisbon 7-11th May, see here for more information.

H

New tutorials on spatial models in INLA

posted 13 Mar 2018, 00:15 by Haakon Bakka   [ updated 11 Apr 2018, 00:44 by Havard Rue ]

I has written several code tutorials on a separate website, see here or hereThe web-page is very much a work in progress, but people find it very helpful, and I use it for teaching INLA courses.

I also added the tutorial "How to solve the stochastic partial differential equation that gives a Matérn random field using the finite element method" , which details the steps for solving the SPDE and computing the resulting matrices.

Feedback is appreciated, and I will update and improve the tutorials if you find them useful.

Best,
Haakon


Shiny application for the analysis of spatial and spatio-temporal count data: SSTCDapp

posted 24 Feb 2018, 20:54 by Havard Rue   [ updated 25 Feb 2018, 04:43 by Andrea Riebler ]

This is cool; hiding the complex interface to R-INLA through an easy web interface....

H


From http://www.unavarra.es/spatial-statistics-group/shiny-app


``SSTCDapp is an interactive web application developed by the Spatial Statistics Group of the Public University of Navarre, Spain supported by grants from the Spanish Ministry of Economy and Competitiveness (Project MTM2014-51992-R) the Health Department of the Navarre Government (Project 113, Res.2186/2014) and the Spanish Ministry of Economy, Industry, and Competitiveness (Project MTM2017-82553-R jointly financed by FEDER) .

SSTCDapp has been designed for the following purposes:

  • To perform descriptive analyses in space and time of mortality/incidence risks or rates.
  • To fit a wide variety of spatial and spatio-temporal hierarchical models commonly used in disease mapping.

The SSTCDapp facilitates the use of these models, since some of them are difficult to implement for non-expert users working in epidemiology or public health institutes. The application may also be used for the analysis of similar problems in many other fields such as criminology, gender-based violence, road traffic accidents or veterinary epidemiology.

The application allows the users to upload their data and its associated cartography, to generate many graphs and tables for the descriptive analysis of mortality/incidence of risks/rates, and last but not least, to fit fairly complex spatio-temporal models to smooth risks in space and time. The recently proposed integrated nested Laplace approximation (INLA) technique for Bayesian inference is used for model fitting and inference using the R-INLApackage.

Start using SSTCDapp''




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