{ "_id": "6a105ea7acfb0bcc41ca5b43", "Package": "ssaBSS", "Type": "Package", "Title": "Stationary Subspace Analysis", "Version": "0.1.2", "Date": "2026-03-30", "Authors@R": "c(person(given = \"Markus\", family = \"Matilainen\", role = c(\"cre\",\"aut\"),\nemail = \"markus.matilainen@outlook.com\",\ncomment = c(ORCID = \"0000-0002-5597-2670\")),\nperson(given = \"Lea\", family = \"Flumian\", role = \"aut\"),\nperson(given = \"Klaus\", family = \"Nordhausen\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-3758-8501\")),\nperson(given = \"Sara\", family = \"Taskinen\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-9470-7258\")))", "Maintainer": "Markus Matilainen ", "Description": "Stationary subspace analysis (SSA) is a blind source\nseparation (BSS) variant where stationary components are\nseparated from non-stationary components. Several SSA methods\nfor multivariate time series are provided here (Flumian et al.\n(2024) ; Hara et al. (2010)\n) along with functions to\nsimulate time series with time-varying variance and\nautocovariance (Patilea and Raissi(2014)\n).", "License": "GPL (>= 2)", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-08 09:21:53 UTC", "User": "root" }, "Author": "Markus Matilainen [cre, aut] (ORCID:\n), Lea Flumian [aut],\nKlaus Nordhausen [aut] (ORCID:\n), Sara Taskinen [aut]\n(ORCID: )", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-04-02 20:58:02 UTC", "RemoteUrl": "https://github.com/cran/ssaBSS", "RemoteRef": "HEAD", "RemoteSha": "1498a708e4849692493debdbbf5423c1e4f2b080", "MD5sum": "555fd24b4ddd75bd9e144b02a23d09c5", "_user": "cran", "_type": "src", "_file": "ssaBSS_0.1.2.tar.gz", "_fileid": "3aef9c00c49625862f74f295832f24ee4b71cfd9e82bd62a13d4e608ae683efc", "_filesize": 252645, "_sha256": "3aef9c00c49625862f74f295832f24ee4b71cfd9e82bd62a13d4e608ae683efc", "_created": "2026-05-08T09:21:53.000Z", "_published": "2026-05-22T13:48:23.039Z", "_distro": 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