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Gradient Boosting for Longitudinal Data

0packages depend
4.4Kdownloads / year
0.0%test coverage
13/13flavors OK

Overview

About

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.

Install

Health

CRAN checks
13OK
Slowest check: 1.5 min · r-oldrel-macos-x86_64
Status changes
  • OK13 OK2026-09-08
  • ERROR12 OK, 1 ERROR2026-09-07
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 0.2
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
6 (3 internal)
Examples that run
100%3 run in a plain check
Exported functions with examples
100%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
0%

Downloads

4.4K
CRAN downloads in the past year
Rank #13,916 · ~12/day · ~368/mo
Daily download trend is not available in this view yet.
25130 days
84890 days
4.4K1 year
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Also on83 r2u2 autocran

Dependencies

Declared dependencies
2 external dependencies (excludes base and recommended)

Installing MEGB adds 1 package (1 with compiled code, 577 KB of source tarballs). Heaviest: gbm 577 KB.

Depends (0)
none
Imports (3)
statsgbmMASS
LinkingTo (0)
none
Suggests (1)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (2)
Author, Maintainer since 0.1 (2025)
Releases (2)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • 0.2Latest
    record
    2025-12-22 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • 0.1
    record
    2025-01-29
  • R
    R 4.4.0 released · 2024-04-24

Package metadata

First published
2025-01-29
Total releases
2 / 1 yrs
License
GPL-2 OSI
Download size
7.9 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("MEGB")
Olaniran, O. R., & Olaniran, S. F. (2025). MEGB: Gradient Boosting for Longitudinal Data (Version 0.2) [Computer software]. https://doi.org/10.32614/CRAN.package.MEGB

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for MEGB version 0.2 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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