MEGB: Gradient Boosting for Longitudinal Data

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.

Version:	0.1
Imports:	stats, gbm, MASS, latex2exp
Suggests:	testthat (≥ 3.0.0)
Published:	2025-01-29
DOI:	10.32614/CRAN.package.MEGB
Author:	Oyebayo Ridwan Olaniran [aut, cre], Saidat Fehintola Olaniran [aut]
Maintainer:	Oyebayo Ridwan Olaniran <olaniran.or at unilorin.edu.ng>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	MEGB results

Documentation:

Reference manual:

MEGB.pdf

Downloads:

Package source:	MEGB_0.1.tar.gz
Windows binaries:	r-devel: not available, r-release: MEGB_0.1.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): MEGB_0.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

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