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Gradient boosting is a powerful statistical learning method known for its ability to model
complex relationships between predictors and outcomes while performing inherent variable selection.
However, traditional gradient boosting methods lack flexibility in handling longitudinal data where
within-subject correlations play a critical role. In this package, we propose a novel approach
Mixed Effect Gradient Boosting ('MEGB'), designed specifically for high-dimensional longitudinal data.
'MEGB' incorporates a flexible semi-parametric model that embeds random effects within the gradient boosting
framework, allowing it to account for within-individual covariance over time. Additionally, the method
efficiently handles scenarios where the number of predictors greatly exceeds the number of observations
(p>>n) making it particularly suitable for genomics data and other large-scale biomedical studies.

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