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.