Genetlib

Improvements
- Delete some useless functions.
- Refine text for CI to improve code coverage to 90%.

Update documentation
- Establish [GENetLib's documentation](https://genetlib.readthedocs.io/en/latest/).
- Update README.md.

Improvements
- Establish [continuous integration](https://github.com/Barry57/GENetLib/actions/workflows/CI.yml) (CI) in workflows.
- Establish [code coverage](https://github.com/Barry57/GENetLib/actions/workflows/code_coverage.yml) in workflows.
- Establish [PyPI](https://github.com/Barry57/GENetLib/actions/workflows/pypi.yml) to automatically upload the newest package to PyPI.
- Create test code with [pytest](https://github.com/Barry57/GENetLib/tree/main/pytest) and conduct tests.
- Utilize code coverage to eliminate unnecessary functions and merged the ``inprod`` and ``inprod_bspline`` functions.

Update documentation
- Add badges into README.

Improvements
- Change the type of output in function ``sim_data_scalar`` into dictionary to make it clear.

Fix bugs
- Rename all functions to comply with the Python community standards.
- Modify the dependencies within the functions as well as the function names in the import statements.

Update documentation
- Modify the examples of ``scalar_ge`` and ``grid_scalar_ge`` in README accordingly.

Improvements
- Introduce metrics for binary output and continuous output in ``ScalarGE``.

Fix bugs
- Fix some bugs in branch statements.

Update documentation
- Update examples of ``GridSNPGE``.
- Detail the parameters in README.

GENetLib`` is a Python library designed for gene-environment interaction analysis via neural network, addressing the analytical challenges in complex disease research.
This package is capable of handling a variety of input data types:
- Scalar input data
- Functional input data (or densely measured data)

This package also supports diverse output requirements:
- Continuous output data
- Binary output data
- Survival output data

By integrating minimax concave penalty (MCP) and $L_2$-norm regularization within a neural network estimation framework, ``GENetLib`` offers an innovative solution for high-dimensional genetic data analysis.

This version is the initial version of the code.