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  "Title": "Gradient Boosting for Longitudinal Data",
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  "Author": "Oyebayo Ridwan Olaniran [aut, cre], Saidat Fehintola Olaniran\n[aut]",
  "Maintainer": "Oyebayo Ridwan Olaniran <olaniran.or@unilorin.edu.ng>",
  "Description": "Gradient boosting is a powerful statistical learning\nmethod known for its ability to model complex relationships\nbetween predictors and outcomes while performing inherent\nvariable selection. However, traditional gradient boosting\nmethods lack flexibility in handling longitudinal data where\nwithin-subject correlations play a critical role. In this\npackage, we propose a novel approach Mixed Effect Gradient\nBoosting ('MEGB'), designed specifically for high-dimensional\nlongitudinal data. 'MEGB' incorporates a flexible\nsemi-parametric model that embeds random effects within the\ngradient boosting framework, allowing it to account for\nwithin-individual covariance over time. Additionally, the\nmethod efficiently handles scenarios where the number of\npredictors greatly exceeds the number of observations (p>>n)\nmaking it particularly suitable for genomics data and other\nlarge-scale biomedical studies.",
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