doi:10.1007/s00362-012-0488-4>). 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 <doi:10.18637/jss.v091.i05>).">

ddalpha: Depth-Based Classification and Calculation of Data Depth (original) (raw)

Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). 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 <doi:10.18637/jss.v091.i05>).

Version: 1.3.16
Depends: R (≥ 2.10), stats, utils, graphics, grDevices, MASS, class, robustbase, sfsmisc, geometry
Imports: Rcpp (≥ 0.11.0)
LinkingTo: BH, Rcpp
Published: 2024-09-30
DOI: 10.32614/CRAN.package.ddalpha
Author: Oleksii Pokotylo [aut, cre], Pavlo Mozharovskyi [aut], Rainer Dyckerhoff [aut], Stanislav Nagy [aut]
Maintainer: Oleksii Pokotylo <alexey.pokotylo at gmail.com>
License: GPL-2
NeedsCompilation: yes
Citation: ddalpha citation info
In views: FunctionalData
CRAN checks: ddalpha results

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