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: It excels at Principal Component Analysis (PCA) and Partial Least Squares (PLS) , allowing users to turn complex, high-dimensional datasets into intuitive 2D "scores plots" to identify clusters and outliers.

: Many universities provide discounted or institutional licenses for SIMCA. Check with your IT department or library before looking elsewhere.

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If you cannot afford SIMCA through any legitimate pathway, use Python or R. The output is identical, the price is zero, and you gain the additional skills of programming and reproducibility that are increasingly required in modern analytical science.

Libraries such as scikit-learn (for PCA and PLS) and specialized chemometrics repositories on GitHub.

DD-SIMCA (Data Driven Soft Independent Modelling of Class Analogies)