Welcome to AnalysisG's Documentation!
======================================

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   introduction
   api/index
   modules/index
   examples
   contributing

AnalysisG - Graph Neural Network Analysis Framework
====================================================

AnalysisG is a comprehensive Graph Neural Network Analysis Framework designed
specifically for High Energy Physics (HEP) applications. The framework provides
tools for event processing, graph construction, model development, and analysis
in particle physics research.

Key Features
------------

* **Event Processing**: Flexible event processing pipeline for HEP data
* **Graph Neural Networks**: Graph-based neural network architectures
* **Model Training**: Complete training infrastructure with metrics
* **Analysis Tools**: Comprehensive analysis and visualization tools
* **Python Integration**: Seamless Python-C++ integration via Cython

Quick Links
-----------

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

Getting Started
---------------

To get started with AnalysisG:

1. Check out the :doc:`introduction` for an overview
2. Browse the :doc:`api/index` for detailed API documentation
3. Explore :doc:`modules/index` to understand the framework architecture
4. See :doc:`examples` for usage examples

License
-------

See LICENSE file in the repository root for licensing information.