v1.1.4

Improvements

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

Update documentation

• Establish GENetLib's documentation.
• Update README.md.

v1.1.3

Improvements

• Establish continuous integration (CI) in workflows.
• Establish code coverage in workflows.
• Establish PyPI to automatically upload the newest package to PyPI.
• Create test code with 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.

v1.0.7

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.

v1.0.5

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.

v1.0.4

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.