{ "_id": "6a2297a7cd65a98ecbd56dee", "Package": "nonstat", "Type": "Package", "Title": "Detecting Nonstationarity in Time Series", "Version": "0.0.6", "Authors@R": "c(\nperson(given = \"Martin\", family = \"Hecht\", email = \"martin.hecht@hsu-hh.de\", role = c(\"aut\", \"cre\")),\nperson(given = \"Steffen\", family = \"Zitzmann\", email = \"steffen.zitzmann@medicalschool-hamburg.de\", role = \"aut\"))", "Maintainer": "Martin Hecht ", "Description": "Provides a nonvisual procedure for screening time series\nfor nonstationarity in the context of intensive longitudinal\ndesigns, such as ecological momentary assessments. The method\ncombines two diagnostics: one for detecting trends (based on\nthe split R-hat statistic from Bayesian convergence\ndiagnostics) and one for detecting changes in variance (a novel\nextension inspired by Levene's test). This approach allows\nresearchers to efficiently and reproducibly detect violations\nof the stationarity assumption, especially when visual\ninspection of many individual time series is impractical. The\nprocedure is suitable for use in all areas of research where\ntime series analysis is central. For a detailed description of\nthe method and its validation through simulations and empirical\napplication, see Zitzmann, S., Lindner, C., Lohmann, J. F., &\nHecht, M. (2024) \"A Novel Nonvisual Procedure for Screening for\nNonstationarity in Time Series as Obtained from Intensive\nLongitudinal Designs\"\n.", "License": "GPL-3", "NeedsCompilation": "no", "Encoding": "UTF-8", "Language": "en-US", "RoxygenNote": "7.3.2", "Repository": "https://martinhecht.r-universe.dev", "Date/Publication": "2025-03-30 20:54:11 UTC", "RemoteUrl": "https://github.com/martinhecht/nonstat", "RemoteRef": "HEAD", "RemoteSha": "ac85f82570bd88be878cca218bbde1a14b8071dc", "Packaged": { "Date": "2026-06-05 09:28:22 UTC", "User": "root" }, "Author": "Martin Hecht [aut, cre],\nSteffen Zitzmann [aut]", "MD5sum": "36aaf886842b55c627968a880563d346", "_user": "martinhecht", "_type": "src", "_file": "nonstat_0.0.6.tar.gz", "_fileid": "6fd584830ce58d6a6fd7d6e23ca49c338a02a906c5dc760a6ff4d26ab42f6b70", "_filesize": 77116, "_sha256": "6fd584830ce58d6a6fd7d6e23ca49c338a02a906c5dc760a6ff4d26ab42f6b70", "_created": "2026-06-05T09:28:22.000Z", "_published": "2026-06-05T09:32:23.816Z", 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