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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
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:

Reference manual: IBLM.html , IBLM.pdf

Downloads:

Package source: IBLM_1.0.1.tar.gz
Windows binaries: r-devel: IBLM_1.0.1.zip, r-release: not available, r-oldrel: IBLM_1.0.1.zip
macOS binaries: r-release (arm64): IBLM_1.0.1.tgz, r-oldrel (arm64): IBLM_1.0.1.tgz, r-release (x86_64): IBLM_1.0.1.tgz, r-oldrel (x86_64): IBLM_1.0.1.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.