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MEGB  

Gradient Boosting for Longitudinal Data
View on CRAN: Click here


Download and install MEGB package within the R console
Install from CRAN:
install.packages("MEGB")

Install from Github:
library("remotes")
install_github("cran/MEGB")

Install by package version:
library("remotes")
install_version("MEGB", "0.1")



Attach the package and use:
library("MEGB")
Maintained by
Oyebayo Ridwan Olaniran
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-01-29
Latest Update: 2025-01-29
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.
How to cite:
Oyebayo Ridwan Olaniran (2025). MEGB: Gradient Boosting for Longitudinal Data. R package version 0.1, https://cran.r-project.org/web/packages/MEGB. Accessed 20 Feb. 2025.
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