IBLM: Interpretable Boosted Linear Models
Implements Interpretable Boosted Linear Models (IBLMs). These combine a conventional generalized linear model (GLM) with a machine learning component, such as XGBoost. The package also provides tools within for explaining and analyzing these models. For more details see Gawlowski and Wang (2025) <https://ifoa-adswp.github.io/IBLM/reference/figures/iblm_paper.pdf>.
| Version: |
1.0.1 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
cli, dplyr, fastDummies, ggExtra, ggplot2, purrr, scales, statmod, stats, utils, withr, xgboost |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2025-11-19 |
| DOI: |
10.32614/CRAN.package.IBLM (may not be active yet) |
| Author: |
Karol Gawlowski [aut, cre, cph],
Paul Beard [aut] |
| Maintainer: |
Karol Gawlowski <Karol.Gawlowski at citystgeorges.ac.uk> |
| BugReports: |
https://github.com/IFoA-ADSWP/IBLM/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://ifoa-adswp.github.io/IBLM/,
https://github.com/IFoA-ADSWP/IBLM |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
IBLM results |
Documentation:
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