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References
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This page credits libraries and assets used by IAML and its documentation.
For references to the methods used by a fitted candidate, see the
:ref:`study bibliography <method-bibliography>`.

Core Libraries and Frameworks
===============================
The principal library references are:

- **Scikit-learn:**
    Scikit-learn: Machine Learning in Python. Available at: https://scikit-learn.org/.

- **SHAP:**
    SHAP (SHapley Additive Explanations): A Unified Approach to Explaining Model Predictions. Available at: https://github.com/slundberg/shap.

- **CatBoost:**
    CatBoost: Gradient Boosting with Categorical Features Support. Available at: https://catboost.ai/.

- **Yellowbrick:**
    Yellowbrick: Visual Analysis and Diagnostic Tools for Machine Learning. Available at: https://www.scikit-yb.org/.

- **cuDF (RAPIDS AI):**
    cuDF: GPU DataFrame Library for Data Manipulation. Available at: https://rapids.ai/.

- **NLTK:**
    NLTK (Natural Language Toolkit): Tools for Working with Human Language Data. Available at: https://www.nltk.org/.

Typography
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The IAML wordmark uses `Bebas Neue <https://github.com/dharmatype/Bebas-Neue>`_,
designed by Ryoichi Tsunekawa, under the SIL Open Font License 1.1.

Documentation animation
=======================

The Discover IAML and Customize IAML pages use
`GSAP and ScrollTrigger <https://gsap.com/docs/v3/Plugins/ScrollTrigger/>`_
under the `GSAP Standard License <https://gsap.com/standard-license/>`_. The
library and its license notice are bundled with the documentation.