MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data - Treder, Matthias S. (Frontiers in Neuroscience, 2020)
MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers (LDA, Logistic Regression, SVM, kernel FDA, Naive Bayes, ensemble methods) and regression models (ridge, kernel ridge), using modern optimization algorithms. High-level functions allow for the multivariate analysis of multi-dimensional data, including generalization (e.g. time x time) and searchlight analysis. The toolbox performs cross-validation, hyperparameter tuning, and nested preprocessing. It computes various classification and regression metrics and establishes their statistical significance. It is modular, easily extendable, and is shipped with sample data and example scripts. Furthermore, it offers interfaces for LIBSVM and LIBLINEAR as well as an integration into the FieldTrip neuroimaging toolbox.