  Modern analysis of high energy physics (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), bagging and random forest algorithms, 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, and a test of zero correlation
between two variables with a joint elliptical distribution are also provided.
The package offers a convenient set of tools for imposing requirements on input
data and displaying output. Integrated in the BaBar computing environment, the
package maintains a minimal set of external dependencies and therefore can be
easily adapted to any other environment. It has been tested on many idealistic
and realistic examples.
