Wrapper of Python Library 'shap'

Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.

Cross-Entropy Clustering

Splits data into Gaussian type clusters using the Cross-Entropy Clustering ('CEC') method. This method allows for the simultaneous use of various types of Gaussian mixture models, for performing the reduction of unnecessary clusters, and for discovering new clusters by splitting them. 'CEC' is based on the work of Spurek, P. and Tabor, J. (2014) .

Discriminant Analysis with Permutation-Invariant Covariance Models

Extends classical linear and quadratic discriminant analysis by incorporating permutation-group symmetries into covariance matrix estimation. Methods based on the 'gips' framework identify and impose permutation structures that regularize covariance estimates and improve stability and interpretability for symmetric or exchangeable features. The package provides pooled and class-specific covariance models, including multi-class variants with shared or independently estimated symmetry structures. The underlying methodology is described by Graczyk et al. (2022) and Chojecki, Morgen, and Kołodziejek (2025) .

Compute SHAP Values for Your Tree-Based Models Using the 'TreeSHAP' Algorithm

An efficient implementation of the 'TreeSHAP' algorithm introduced by Lundberg et al., (2020) . It is capable of calculating SHAP (SHapley Additive exPlanations) values for tree-based models in polynomial time. Currently supported models include 'gbm', 'randomForest', 'ranger', 'xgboost', 'lightgbm'.

Surrogate-Assisted Feature Extraction

Provides a model agnostic tool for white-box model trained on features extracted from a black-box model. For more information see: Gosiewska et al. (2020) .

Interactive Studio for Explanatory Model Analysis

Automate the explanatory analysis of machine learning predictive models. Generate advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks. The main function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. It is possible to easily save the dashboard and share it with others. modelStudio facilitates the process of Interactive Explanatory Model Analysis introduced in Baniecki et al. (2023) .

Generate Tidy Charts Inspired by 'IBCS'

There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS'). It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments. We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible. All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'.

Explainable Machine Learning in Survival Analysis

Survival analysis models are commonly used in medicine and other areas. Many of them are too complex to be interpreted by human. Exploration and explanation is needed, but standard methods do not give a broad enough picture. 'survex' provides easy-to-apply methods for explaining survival models, both complex black-boxes and simpler statistical models. They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023) , SurvLIME described in Kovalev et al., (2020) as well as extensions of existing ones described in Biecek et al., (2021) .

Shared Utilities to Extend the 'teal' Modules

Provides decorators, transformators, and utility functions to extend the 'teal' framework for interactive data analysis applications. Implements methods for data visualization enhancement, statistical data transformations, and workflow integration tools. Designed to support clinical and pharmaceutical research workflows within the 'teal' ecosystem through modular and reusable components.

R Bindings to the 'Fstlib' Library

The 'fstlib' library provides multithreaded serialization of compressed data frames using the 'fst' format. The 'fst' format allows for random access of stored data and compression with the 'LZ4' and 'ZSTD' compressors.