ddalpha (1.3.11)

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Depth-Based Classification and Calculation of Data Depth.


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. (Pokotylo, Mozharovskyi and Dyckerhoff, 2019 ).

Maintainer: Oleksii Pokotylo
Author(s): Oleksii Pokotylo [aut, cre], Pavlo Mozharovskyi [aut], Rainer Dyckerhoff [aut], Stanislav Nagy [aut]

License: GPL-2

Uses: class, geometry, MASS, Rcpp, robustbase, sfsmisc
Reverse depends: curveDepth, TukeyRegion
Reverse suggests: butcher, recipes

Released 15 days ago.

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