r-cran-ddalpha - 1.3.16-1 main

Contains procedures for depth-based supervised learning, which are
entirely non-parametric, in particular the DDalpha-procedure (Lange,
Mosler and Mozharovskyi, 2014). The training data sample is transformed
by a statistical depth function to a compact low-dimensional space,
where the final classification is done. It also offers an extension to
functional data and routines for calculating certain notions of
statistical depth functions. 50 multivariate and 5 functional
classification problems are included.