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Longitudinal data refer to the situation where repeated observations are available for each sampled individual. Methodologies that take this structure into account allow for systematic differences between individuals that are not related to covariates. A standard methodology in the statistics literature for this type of data is the random effects model, where these differences between individuals are represented by so-called "Effects" that are estimated from the data. This paper presents a methodology that combines the flexibility of tree-based estimation methods with the structure of random effects models for longitudinal data.
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