A framework for two-class classification problems, with
applications to differential variability and differential
distribution testing
Description
The software formalises a framework for classification in R.
There are four stages; Data transformation, feature selection, classifier training,
and prediction. The requirements of variable types and names are
fixed, but specialised variables for functions can also be provided.
The classification framework is wrapped in a driver loop, that
reproducibly carries out a number of cross-validation schemes.
Functions for differential expression, differential variability,
and differential distribution are included. Additional functions
may be developed by the user, by creating an interface to the framework.