ale Package

Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has two primary advantages over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values are not affected by the presence of interactions among variables in a model and its computation is relatively rapid. This package rewrites the original code from the ‘ALEPlot’ package for calculating ALE data and it completely reimplements the plotting of ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference.

For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv.

The ale package replicates the full functionality of the ALEPlot package and a lot more. It currently presents three main functions:

In addition, it has some minor functions that are helpful for model evaluation.

You may find more details in the following vignettes (they are all available from the vignettes link on the main CRAN page at