StatPatternRecognition: A C++ Package for Multivariate Classification of

Modern analysis of HEP data needs advanced statistical tools to separate signal from background. A C++ package has been implemented to provide such tools for the HEP community. The package includes linear and quadratic discriminant analysis, decision trees, bump hunting (PRIM), boosting (AdaBoost and arc-x4), bagging and random forest algorithms, a multi-class learner, and interfaces to the standard backpropagation neural net and radial basis function neural net implemented in the Stuttgart Neural Network Simulator. Supplemental tools such as bootstrap, estimation of data moments, a test of zero correlation between two variables with a joint elliptical distribution, and a multivariate goodness-of-fit method are also provided. The package offers a convenient set of tools for imposing requirements on input data, storing output into Root or Hbook, and handling multi-class data. Integrated in the BABAR computing environment, the package maintains a minimal set of external dependencies and can be easily adapted to any other HEP environment. It has been tested on many idealistic and realistic examples