Life & fun · December 23, 2023
4d-Modeller – R library to make Bayesian spatiotemporal modeling easy
Hi, I've been working with other colleagues at University of Bristol for the last year to build an R-package that makes bayesian spatio-temporal modeling easy. That is, if you have some problem like predicting sea level rise over time, disease transmissions, etc., this package may be useful for you. It centers on a collection of Shiny apps that guide you through using the various pieces of spatio-temporal modeling packages like R-INLA or inlabru. Hopefully people find this useful. I think the nice part is that we really tried to design the package so you can add it to your modeling tool kit…
In plain words
4d-Modeller is an R package developed at the University of Bristol that simplifies Bayesian spatiotemporal modeling through a collection of Shiny apps. It guides users through tools like R-INLA and inlabru for applications such as predicting sea level rise or disease transmission. The package is designed to integrate into existing modeling workflows without requiring extensive R programming knowledge.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi, I've been working with other colleagues at University of Bristol for the last year to build an R-package that makes bayesian spatio-temporal modeling easy. That is, if you have some problem like predicting sea level rise over time, disease transmissions, etc., this package may be useful for you. It centers on a collection of Shiny apps that guide you through using the various pieces of spatio-temporal modeling packages like R-INLA or inlabru. Hopefully people find this useful. I think the nice part is that we really tried to design the package so you can add it to your modeling tool kit without having to learn a lot about R to be useful with the tool. Hopefully we succeeded. We will have a hackathon for this model in March 25-27: https://4dmodeller.github.io/4DM_Hackathon/ At the hackathon we will implement new features or apply the model to new applications.
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