PLSToolbox - Eigenvector Research

PLSToolbox - Eigenvector Research

It is a suite of essential and advanced chemometric multivariate analysis tools
 
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PLS_Toolbox software is the world’s most extensive suite of essential and advanced chemometric multivariate analysis tools for use within the MATLAB® computational environment. Not a MATLAB® user? Many of the same powerful tools area available with our stand-alone product, Solo.

PLS_Toolbox provides a unified graphical interface and over 300 tools for use in a wide variety of technical areas. It takes its name from the Partial Least Squares (PLS) regression method, which has become the standard calibration method in many regression applications, but offers so much more. It contains all the software tools chemical engineers, analytical chemists and other analysis-driven scientists require to fully utilize their data and build predictive models.

Key Features:
Data Exploration and Pattern Recognition (Principal Components Analysis (PCA), Parallel Factor Analysis (PARAFAC), Multiway PCA, Tucker Models...)
Classification (SIMCA, k-nearest neighbors, PLS Discriminant Analysis, Support Vector Machine Classification, Clustering (HCA)...)
Linear and Non-Linear Regression (PLS, Principal Components Regression (PCR), Multiple Linear Regression (MLR), Classical Least Squares (CLS), Support Vector Machine Regression, N-way PLS, Locally Weighted Regression, Polynomial PLS...)
Design of Experiment (DOE) tools for designing and analyzing experiments
Self-modeling Curve Resolution, Pure Variable Methods (Multivariate Curve Resolution (MCR), Purity (compare to SIMPLSMA), CODA_DW, CompareLCMS...)
Curve fitting and Distribution fitting and analysis tools
Instrument Standardization (Piece-wise Direct, Windowed Picewise, OSC, Generalized Least Squares Preprocessing...)
Advanced Graphical Data Set Editing and Visualization Tools
Advanced Customizable Order-Specific Preprocessing (Centering, Scaling, Smoothing, Derivatizing, Transformations, Baselining...)
Missing Data Support (SVD and NIPALS)
Variable Selection (Genetic algorithms, IPLS, Selectivity, VIP...)

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