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: | 1.0.2 |
| Depends: | R (≥ 4.1.0) |
| Imports: | cli,dplyr,fastDummies,ggExtra,ggplot2,purrr,scales,statmod, stats, utils,withr,xgboost (≥ 3.1.2.1) |
| Suggests: | knitr,rmarkdown,testthat (≥ 3.0.0),gt,patchwork |
| Published: | 2025-12-16 |
| DOI: | 10.32614/CRAN.package.IBLM |
| 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 + fileLICENSE |
| URL: | https://ifoa-adswp.github.io/IBLM/,https://github.com/IFoA-ADSWP/IBLM |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | IBLM results |