The Toolkit for Multivariate Analysis (TMVA) provides a
ROOT-integrated environment for the parallel processing and
evaluation of MVA techniques to discriminate signal from background
samples. It presently includes (ranked by complexity):

* Rectangular cut optimization
* Correlated likelihood estimator (PDE approach)
* Multi-dimensional likelihood estimator (PDE - range-search approach)
* Fisher (and Mahalanobis) discriminant
* H-Matrix (chi-squared) estimator
* Artificial Neural Network (two different implementations)
* Boosted Decision Trees

The TMVA package includes an implementation for each of these
discrimination techniques, their training and testing (performance
evaluation). In addition all these methods can be tested in parallel,
and hence their performance on a particular data set may easily be
compared.