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IBLM

Interpretable Boosted Linear Models

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

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")

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",
  family = "poisson"
)

ex <- explain_iblm(iblm_model, df_list$test)

Documentation

For Documentation on the various functions in this package visit:

🔗 https://ifoa-adswp.github.io/IBLM/

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

Citation

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

Gawlowski, K. and Beard, P. (2025). IBLM: Interpretable Boosted Linear Models. R package version 1.0.0.

License

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

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.
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