{ "_id": "6a1d27a21d7bb097a0a38284", "Package": "Rbeast", "Type": "Package", "Version": "1.0.1", "Date": "2024-02-08", "Title": "Bayesian Change-Point Detection and Time Series Decomposition", "Authors@R": "c(person(given = \"Tongxi\", family = \"Hu\", role = \"aut\", email = \"hu.1555@osu.edu\"),\nperson(given = \"Yang\", family = \"Li\", role = \"aut\", email = \"li.10015@osu.edu\"),\nperson(given = \"Xuesong\", family = \"Zhang\", role = \"aut\", email= \"xuesong.zhang@usda.gov\" ),\nperson(given = \"Kaiguang\", family = \"Zhao\", role = c(\"aut\", \"cre\"), email = \"zhao.1423@osu.edu\"),\nperson(given = \"Jack\", family = \"Dongarra\", role = \"ctb\"),\nperson(given = \"Cleve\", family = \"Moler\", role = \"ctb\")\n)", "Author": "Tongxi Hu [aut], Yang Li [aut], Xuesong Zhang [aut], Kaiguang\nZhao [aut, cre], Jack Dongarra [ctb], Cleve Moler [ctb]", "Maintainer": "Kaiguang Zhao ", "Description": "Interpretation of time series data is affected by model\nchoices. Different models can give different or even\ncontradicting estimates of patterns, trends, and mechanisms for\nthe same data--a limitation alleviated by the Bayesian\nestimator of abrupt change,seasonality, and trend (BEAST) of\nthis package. BEAST seeks to improve time series decomposition\nby forgoing the \"single-best-model\" concept and embracing all\ncompeting models into the inference via a Bayesian model\naveraging scheme. It is a flexible tool to uncover abrupt\nchanges (i.e., change-points), cyclic variations (e.g.,\nseasonality), and nonlinear trends in time-series observations.\nBEAST not just tells when changes occur but also quantifies how\nlikely the detected changes are true. It detects not just\npiecewise linear trends but also arbitrary nonlinear trends.\nBEAST is applicable to real-valued time series data of all\nkinds, be it for remote sensing, economics, climate sciences,\necology, and hydrology. Example applications include its use to\nidentify regime shifts in ecological data, map forest\ndisturbance and land degradation from satellite imagery, detect\nmarket trends in economic data, pinpoint anomaly and extreme\nevents in climate data, and unravel system dynamics in\nbiological data. Details on BEAST are reported in Zhao et al.\n(2019) .", "LazyData": "true", "License": "GPL (>= 2)", "URL": "https://github.com/zhaokg/Rbeast", "NeedsCompilation": "yes", "Repository": "https://zhaokg.r-universe.dev", "Date/Publication": "2026-04-30 01:48:28 UTC", "RemoteUrl": "https://github.com/zhaokg/rbeast", "RemoteRef": "HEAD", "RemoteSha": "f9e7e53867c18713330828779f580dbe28be34d4", "RemoteSubdir": "R", "Packaged": { "Date": "2026-05-30 05:28:49 UTC", "User": "root" }, "MD5sum": "8cbcd4bc49ffc14cc68d9f557f477129", "_user": "zhaokg", "_type": "src", "_file": "Rbeast_1.0.1.tar.gz", "_fileid": "13b54757c486578ab2360d9c6cc1c69f69e5f5a262b812a3d54011c962bf6c9f", "_filesize": 3436448, "_sha256": "13b54757c486578ab2360d9c6cc1c69f69e5f5a262b812a3d54011c962bf6c9f", "_created": "2026-05-30T05:28:49.000Z", "_published": "2026-06-01T06:33:06.275Z", "_distro": "noble", "_jobs": [ { "job": 78798332004, "time": 140, "config": "linux-devel-arm64", "r": "4.7.0", "check": "FAIL", "artifact": "" 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