DepthProc

R package DepthProc: Statistical Depth Functions for Multivariate Analysis. Data depth concept offers a variety of powerful and user friendly tools for robust exploration and inference for multivariate data. The offered techniques may be successfully used in cases of lack of our knowledge on parametric models generating data due to their nonparametric nature. The package consist of among others implementations of several data depth techniques involving multivariate quantile-quantile plots, multivariate scatter estimators, multivariate Wilcoxon tests and robust regressions.

Keywords for this software

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References in zbMATH (referenced in 9 articles , 1 standard article )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Leuenberger, Michael; Eustache, Esther; Jauslin, Raphaël; Tillé, Yves: Balancing a sample almost perfectly (2022)
  2. Pearson, Ronald K.: Mining imperfect data. With examples in R and Python (2020)
  3. Kosiorowski, Daniel; Rydlewski, Jerzy P.; Snarska, Małgorzata: Detecting a structural change in functional time series using local Wilcoxon statistic (2019)
  4. Oleksii Pokotylo; Pavlo Mozharovskyi; Rainer Dyckerhoff: Depth and Depth-Based Classification with R Package ddalpha (2019) not zbMATH
  5. Wang, Jin: Asymptotics of generalized depth-based spread processes and applications (2019)
  6. Dutta, Subhajit; Sarkar, Soham; Ghosh, Anil K.: Multi-scale classification using localized spatial depth (2016)
  7. Kosiorowski, Daniel: Dilemmas of robust analysis of economic data streams (2016)
  8. Kosiorowski, Daniel: Two procedures for robust monitoring of probability distributions of economic data stream induced by depth functions (2015)
  9. Daniel Kosiorowski, Zygmunt Zawadzki: DepthProc An R Package for Robust Exploration of Multidimensional Economic Phenomena (2014) arXiv