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A spatio-temporal modelling package that can be applied to problems at any scale, from micro to processes that operate at global scale.

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Seamlessly model continuous processes in R. 

4D Modeller includes data visualization tools, finite element mesh building tools, Bayesian hierarchical modelling based on Bayesian inference packages INLA and inlabru, and model evaluation and assessment tools.

Designed for Modelling

4DModeller has been designed to make it easy to design spatially distributed, temporally dependent statistical models. Typically, 4DModeller expects tabular data sets with spatial coordinates, time indices, and the values that change or remain constant over those times. It is designed to be used in the modelling process once data has been sufficiently organized from wherever it was gathered from.

Areas of Usage

This package is typically used to model continuous processes (e.g., sea level rises, earth’s magnetic field), probability distributions of point processes (e.g., earthquake locations), or residuals.

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4D Modeller was developed in partnership with University of Bristol, University of Oslo Njord Center, TU Munich and Expert Analytics.

Curious to learn more about how 4D Modeller can be used for spatio-temporal modelling?

Visit the website

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