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Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation routines. It is written in C++.
A ROOT-integrated toolkit for multivariate analysis
TMVA is a ROOT-integrated toolkit for multivariate classification and regression analysis. TMVA performs the training, testing and performance evaluation of a large variety of multivariate methods.
Since 2013, TMVA has been fully integrated with ROOT and is distributed as part of it. The new homepage of TMVA is https://root.cern
GMM-GMR is a light package of functions in C/C++ to compute Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR). It allows to encode any dataset in a GMM, and GMR can then be used to retrieve partial data by specifying the desired inputs.