https://sgoerz.r-universe.devRecent package updates in sgoerzR-universehttps://github.com/sgoerz.pnghttps://sgoerz.r-universe.devTue, 26 May 2026 13:57:21 GMTsheila.goerz@tu-dortmund.de (Sheila Goerz)Provides methods to detect structural changes in time series or random fields (spatial data). Focus is on the detection of abrupt changes or trends in independent data, but the package also provides a function to de-correlate data with dependence. The functions are based on the test suggested in Schmidt (2024) <DOI:10.3150/23-BEJ1686> and the work in Görz and Fried (2025) <DOI:10.48550/arXiv.2512.11599>.https://github.com/r-universe/sgoerz/actions/runs/26459089358Tue, 26 May 2026 13:57:21 GMTSChangeBlock0.1.1successhttps://sgoerz.r-universe.devhttps://github.com/sgoerz/schangeblocksheila.goerz@tu-dortmund.de (Sheila Goerz)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>.https://github.com/r-universe/sgoerz/actions/runs/27124046542Fri, 09 Jan 2026 13:04:32 GMTrobcp0.3.10successhttps://sgoerz.r-universe.devhttps://github.com/sgoerz/robcp