R package ddalpha. 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.
Keywords for this software
References in zbMATH (referenced in 11 articles , 1 standard article )
Showing results 1 to 11 of 11.
- Cascos, Ignacio; Ochoa, Maicol: Expectile depth: theory and computation for bivariate datasets (2021)
- Dyckerhoff, Rainer; Mozharovskyi, Pavlo; Nagy, Stanislav: Approximate computation of projection depths (2021)
- Nagy, Stanislav; Helander, Sami; van Bever, Germain; Viitasaari, Lauri; Ilmonen, Pauliina: Flexible integrated functional depths (2021)
- Oluwasegun Ojo, Rosa E. Lillo, Antonio Fernández Anta: Outlier Detection for Functional Data with R Package fdaoutlier (2021) arXiv
- Tat, Samaneh; Faridrohani, Mohammad Reza: A new type of multivariate records: depth-based records (2021)
- Klein, Nadja; Kneib, Thomas: Directional bivariate quantiles: a robust approach based on the cumulative distribution function (2020)
- Dehghan, Sakineh; Faridrohani, Mohammad Reza: Affine invariant depth-based tests for the multivariate one-sample location problem (2019)
- Nagy, Stanislav; Ferraty, Frédéric: Data depth for measurable noisy random functions (2019)
- Oleksii Pokotylo; Pavlo Mozharovskyi; Rainer Dyckerhoff: Depth and Depth-Based Classification with R Package ddalpha (2019) not zbMATH
- Pokotylo, Oleksii; Mosler, Karl: Classification with the pot-pot plot (2019)
- Mozharovskyi, Pavlo; Mosler, Karl; Lange, Tatjana: Classifying real-world data with the (DD\alpha)-procedure (2015)