Package: MEGB
Type: Package
Title: Gradient Boosting for Longitudinal Data
Version: 0.2
Authors@R: c(person("Oyebayo Ridwan", "Olaniran", role = c("aut", "cre"),
                     email = "olaniran.or@unilorin.edu.ng"),
              person("Saidat Fehintola", "Olaniran", role = "aut",
              email = "saidat.olaniran@kwasu.edu.ng"))
Author: Oyebayo Ridwan Olaniran [aut, cre],
  Saidat Fehintola Olaniran [aut]
Maintainer: Oyebayo Ridwan Olaniran <olaniran.or@unilorin.edu.ng>
Description: 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.
License: GPL-2
Encoding: UTF-8
Imports: stats, gbm, MASS
NeedsCompilation: no
RoxygenNote: 7.3.2.9000
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Packaged: 2025-12-21 22:30:24 UTC; USER
Repository: CRAN
Date/Publication: 2025-12-21 23:00:02 UTC
Built: R 4.6.0; ; 2026-04-26 04:09:47 UTC; unix
