Partial Least Squares (PLS) Regression with NIPALS Algorithm Implementation

Partial Least Squares (PLS) regression finds extensive applications across multiple scientific and engineering domains. This package delivers a robust PLS regression function utilizing the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm, which efficiently handles covariance structures in high-dimensional data through iterative decomposition of predictor and response matrices. The implementation features matrix standardization, component extraction, and regression coefficient calculation with convergence controls. Additionally, the package includes comprehensive tutorial materials that demonstrate the NIPALS algorithm's mathematical foundation, step-by-step execution process, and parameter optimization techniques. This toolkit significantly enhances data processing and regression analysis efficiency, particularly when handling large-scale datasets with multicollinearity issues, by providing optimized computational routines and clear implementation guidelines.