A Simple PLS Program with Basic Data Processing Capabilities

Hello everyone, I'd like to share a PLS program I recently developed. This implementation focuses on fundamental Partial Least Squares algorithms, featuring basic dimensionality reduction and regression capabilities through covariance maximization between input and output variables.

The core functionality involves processing input datasets to extract specific information patterns. Despite its simplicity, the program handles data preprocessing, component calculation, and predictive modeling through the NIPALS algorithm - making it particularly useful for researchers and data analysts working with high-dimensional data where multicollinearity is a concern.

The modular code structure allows for customization according to specific needs. Experienced programmers can easily extend functionality by modifying the component extraction loop or adding cross-validation routines. Key parameters like the number of latent components can be adjusted in the configuration section.

I welcome feedback on both the algorithm implementation and code structure. Your input on computational efficiency, statistical accuracy, or potential enhancements will help improve this tool for broader applications in chemometrics, bioinformatics, and multivariate analysis.