{ "_id": "6ad74d2f0d12c08e591b8d6b", "Package": "tsqn", "Type": "Package", "Title": "Applications of the Qn Estimator to Time Series (Univariate and\nMultivariate)", "Version": "1.2.0", "Date": "2026-03-16", "Authors@R": "c(\nperson(given = \"Higor\",\nfamily = \"Cotta\",\nrole = c(\"aut\", \"cre\"),\nemail = \"cotta.higor@gmail.com\"),\nperson(given = \"Valderio\",\nfamily = \"Reisen\",\nrole = \"aut\"),\nperson(given = \"Pascal\",\nfamily = \"Bondon\",\nrole = \"aut\"),\nperson(given = \"Céline\",\nfamily = \"Lévy-Leduc\",\nrole = \"aut\")\n)", "Description": "Time Series Qn is a package with applications of the Qn\nestimator of Rousseeuw and Croux (1993)\n to univariate and\nmultivariate Time Series in time and frequency domains. More\nspecifically, the robust estimation of autocorrelation or\nautocovariance matrix functions from Ma and Genton (2000, 2001)\n, and\nCotta (2017) are provided.\nThe robust pseudo-periodogram of Molinares et. al. (2009)\n is also given. This packages\nalso provides the M-estimator of the long-memory parameter d\nbased on the robustification of the GPH estimator proposed by\nReisen et al. (2017) .", "License": "GPL (>= 2)", "VignetteBuilder": "knitr", "LazyData": "true", "Config/testthat/edition": "3", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "Repository": "https://rogih.r-universe.dev", "Date/Publication": "2026-03-16 21:29:12 UTC", "RemoteUrl": "https://github.com/rogih/tsqn", "RemoteRef": "HEAD", "RemoteSha": "23068cbf3d48c52eb346667b5f4df1350513227d", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-20 10:51:59 UTC", "User": "root" }, "Author": "Higor Cotta [aut, cre],\nValderio Reisen [aut],\nPascal Bondon [aut],\nCéline Lévy-Leduc [aut]", "Maintainer": "Higor Cotta ", "_user": "rogih", "_type": "src", "_file": "tsqn_1.2.0.tar.gz", "_fileid": "https://r2.ropensci.org/a5c16147ea5d311a529055f27141ae4046523c8d1e241ae7b56318035acc9720", "_filesize": 259279, "_sha256": "a5c16147ea5d311a529055f27141ae4046523c8d1e241ae7b56318035acc9720", "_expires": "2027-01-28T11:14:53.000Z", "_created": "2026-10-20T10:51:59.000Z", "_published": "2026-10-20T11:14:55.322Z", 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