ddalpha: 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 <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: | AnomalyDetection,FunctionalData |
| CRAN checks: | ddalpha results |
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