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: |
1.3.11 |
| Depends: |
R (≥ 2.10), stats, utils, graphics, grDevices, MASS, class, robustbase, sfsmisc, geometry |
| Imports: |
Rcpp (≥ 0.11.0) |
| LinkingTo: |
BH, Rcpp |
| Published: |
2020-01-09 |
| 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 |
| SystemRequirements: |
C++11 |
| Citation: |
ddalpha citation info |
| In views: |
FunctionalData |
| CRAN checks: |
ddalpha results |
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