💻 Related Software:
====================

``shapiq`` is a Library for computing Shapley Interactions and Shapley
Values for Machine Learning :cite:p:`Muschalik.2024b`. The following
table contains a list of related software:

+------------------+-------------------------------+-------------------------------------------------------+
| Software         | Citation                      | Description                                           |
+==================+===============================+=======================================================+
| ``shapiq``       | :cite:p:`Muschalik.2024b`     | This Python library for interpreting machine learning |
|                  |                               | models with general game-theoretic concpts such as    |
|                  |                               | Shapley interactions, Shapley values, or Banzhaf      |
|                  |                               | values.                                               |
+------------------+-------------------------------+-------------------------------------------------------+
| ``shap``         | :cite:p:`Lundberg.2017`       | A Python library for interpreting machine learning    |
|                  |                               | models, including Shapley values.                     |
+------------------+-------------------------------+-------------------------------------------------------+
| ``OpenXAI``      | :cite:p:`Agarwal.2022`        | A benchmark suite for Explainable AI                  |
+------------------+-------------------------------+-------------------------------------------------------+
| ``iNNvestigate`` | :cite:p:`Alber.2019`          | An Interpretability Toolkit for Neural Networks       |
+------------------+-------------------------------+-------------------------------------------------------+
| ``aix360``       | :cite:p:`Arya.2020`           | An Extensible Toolkit for Understanding Data and      |
|                  |                               | Machine Learning Models                               |
+------------------+-------------------------------+-------------------------------------------------------+
| ``dalex``        | :cite:p:`Baniecki.2021`       | Responsible Machine Learning with Interactive         |
|                  |                               | Explainability and Fairness in Python                 |
+------------------+-------------------------------+-------------------------------------------------------+
| ``openml``       | :cite:p:`Bischl.2021`         | A general purpose Benchmarking Suites                 |
+------------------+-------------------------------+-------------------------------------------------------+
| ``quantus``      | :cite:p:`Hedstrom.2023`       | An Explainable AI Toolkit for Responsible Evaluation  |
|                  |                               | of Neural Network Explanations and Beyond             |
+------------------+-------------------------------+-------------------------------------------------------+
| ``OpenDataVal``  | :cite:p:`Jiang.2023`          | A a Unified Benchmark for Data Valuation              |
+------------------+-------------------------------+-------------------------------------------------------+
| ``alibi``        | :cite:p:`Klaise.2021`         | General Algorithms for Explaining Machine Learning    |
|                  |                               | Models                                                |
+------------------+-------------------------------+-------------------------------------------------------+
| ``captum``       | :cite:p:`Kokhlikyan.2020`     | A unified and generic model interpretability library  |
|                  |                               | for PyTorch                                           |
+------------------+-------------------------------+-------------------------------------------------------+
| ``XAI-bench``    | :cite:p:`Liu.2021`            | Synthetic Benchmarks for Scientific Research in       |
|                  |                               | Explainable Machine Learning                          |
+------------------+-------------------------------+-------------------------------------------------------+
| ``M4``           | :cite:p:`Li.2023`             | A Unified XAI Benchmark for Faithfulness Evaluation   |
|                  |                               | of Feature Attribution Methods across Metrics,        |
|                  |                               | Modalities and Models                                 |
+------------------+-------------------------------+-------------------------------------------------------+
|                  | :cite:p:`Olsen.2024`          | A comparative study of methods for estimating         |
|                  |                               | model-agnostic Shapley value explanations             |
+------------------+-------------------------------+-------------------------------------------------------+