IBLM

Interpretable Boosted Linear Models

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Overview

IBLM implements Interpretable Boosted Linear Models — a hybrid modelling approach that combines the transparency of generalized linear models (GLMs) with the predictive power of gradient boosting.

The package provides:

• Functions for fitting interpretable boosted linear models
• Tools to analyze and visualize model results
• Support for model comparison and diagnostics

This is an R package. However an equivalent Python package is available too: 🔗 https://ifoa-adswp.github.io/pyIBLM

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Installation

You can install the released version of IBLM from CRAN:

install.packages("IBLM")

You can install the development version from GitHub:

# install.packages("remotes")
# remotes::install_github("IFoA-ADSWP/IBLM")
install.packages("IBLM", repos = c('https://ifoa-adswp.r-universe.dev', 'https://cloud.r-project.org'))

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Example

Here’s a minimal example to train and explain an IBLM:

library(IBLM)

df_list <- freMTPLmini  |>
  split_into_train_validate_test()

iblm_model <- train_iblm_xgb(
  df_list,
  response_var = "ClaimRate",
  weight_var = "Exposure",
  family = "quasipoisson"
)

ex <- explain_iblm(iblm_model, df_list$test)

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Documentation

For Documentation on the various functions in this package visit:

🔗 https://ifoa-adswp.github.io/IBLM/

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Contributing

Contributions are welcome!
If you’d like to report a bug or suggest a feature, please open an issue on GitHub:

🔗 https://github.com/IFoA-ADSWP/IBLM/issues

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Citation

If you use IBLM in research or teaching, please cite it as:

Gawlowski, K. and Beard, P. (2026). IBLM: Interpretable Boosted Linear Models. R package version 2.0.1.

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License

This package is licensed under the MIT License.
See the LICENSE file for full details.