Robust Nonparametric Two-Sample Tests for Location/Scale

Implementations of several robust nonparametric two-sample tests for location or scale differences. The test statistics are based on robust location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators as described in Fried & Dehling (2011) . The p-values can be computed via the permutation principle, the randomization principle, or by using the asymptotic distributions of the test statistics under the null hypothesis, which ensures (approximate) distribution independence of the test decision. To test for a difference in scale, we apply the tests for location difference to transformed observations; see Fried (2012) . Random noise on a small range can be added to the original observations in order to hold the significance level on data from discrete distributions. The location tests assume homoscedasticity and the scale tests require the location parameters to be zero.


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1.0.0 by Sermad Abbas, a month ago

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Authors: Sermad Abbas [aut, cre] , Barbara Brune [aut] , Roland Fried [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rdpack, gtools, robustbase, statmod, stats, utils, checkmate

Suggests testthat, knitr, rmarkdown, usethis, covr

See at CRAN