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.


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Reference manual

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install.packages("ddalpha")

1.3.8 by Oleksii Pokotylo, a month ago


Browse source code at https://github.com/cran/ddalpha


Authors: Oleksii Pokotylo [aut, cre] , Pavlo Mozharovskyi [aut] , Rainer Dyckerhoff [aut] , Stanislav Nagy [aut]


Documentation:   PDF Manual  


Task views: Functional Data Analysis


GPL-2 license


Imports Rcpp

Depends on stats, utils, graphics, grDevices, MASS, class, robustbase, sfsmisc, geometry

Linking to BH, Rcpp

System requirements: C++11


Imported by pdSpecEst.

Depended on by TukeyRegion, curveDepth.

Suggested by recipes.


See at CRAN