robcp: Robust Change-Point Tests
Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in ___location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <doi:10.48550/arXiv.1905.06201>, and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) <doi:10.1007/978-1-4939-3076-0_12> and Dehling, Fried and Wendler (2020) <doi:10.1093/biomet/asaa004>. Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) <doi:10.1080/01621459.2019.1629938>, Dehling et al. (2017) <doi:10.1017/S026646661600044X>, and Wied et al. (2014) <doi:10.1016/j.csda.2013.03.005>.
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