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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.
WWW: https://cran.r-project.org/web/packages/ddalpha/
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