Comprehensive MATLAB Toolbox from Abroad

This article introduces a comprehensive MATLAB toolbox developed internationally, which incorporates essential statistical and machine learning algorithms including Multiple Linear Regression (MLR), Principal Component Analysis (PCA), Partial Least Squares (PLS), and Kernel Principal Component Regression (KPCR). The toolbox provides robust implementations of these algorithms with optimized matrix operations and numerical computation techniques, enabling users to perform efficient data analysis and draw more accurate conclusions. Key functions feature parameter customization options through structured input arguments, allowing users to adjust algorithm parameters such as convergence thresholds, kernel types for KPCR, and component numbers for PCA/PLS. The toolbox significantly streamlines complex data processing tasks through pre-built functions that handle data normalization, cross-validation, and result visualization, enabling researchers to focus more time on data interpretation and analytical insights rather than procedural coding.