{ "_id": "6a154111acfb0bcc41d55c75", "Package": "TCIU", "Title": "Spacekime Analytics, Time Complexity and Inferential Uncertainty", "Version": "1.2.8", "Authors@R": "c(person(\"Yongkai\", \"Qiu\", role = \"aut\", email = \"yongkai@umich.edu\"),\nperson(\"Zhe\", \"Yin\", role = \"aut\"),\nperson(\"Jinwen\", \"Cao\", role = \"aut\"),\nperson(\"Yupeng\", \"Zhang\", role = \"aut\"),\nperson(\"Yuyao\", \"Liu\", role = \"aut\"),\nperson(\"Rongqian\", \"Zhang\", role = \"aut\"),\nperson(\"Yueyang\", \"Shen\", role = c(\"aut\", \"cre\"), email = \"petersyy@umich.edu\"),\nperson(\"Rouben\", \"Rostamian\", role = \"ctb\"),\nperson(\"Ranjan\", \"Maitra\", role = \"ctb\"),\nperson(\"Daniel\", \"Rowe\", role = \"ctb\"),\nperson(\"Daniel\", \"Adrian\", role = \"ctb\", comment = \"gLRT method for complex-valued fMRI statistics\"),\nperson(\"Yunjie\", \"Guo\", role = \"aut\", email = \"jerryguo@umich.edu\"),\nperson(\"Ivo\",\"Dinov\", role = \"aut\", email = \"statistics@umich.edu\"))", "URL": "https://github.com/SOCR/TCIU,\nhttps://www.socr.umich.edu/spacekime/,\nhttps://www.socr.umich.edu/TCIU/", "BugReports": "https://github.com/SOCR/TCIU/issues", "Description": "Provide the core functionality to transform longitudinal\ndata to complex-time (kime) data using analytic and numerical\ntechniques, visualize the original time-series and\nreconstructed kime-surfaces, perform model based (e.g.,\ntensor-linear regression) and model-free classification and\nclustering methods in the book Dinov, ID and Velev, MV. (2021)\n\"Data Science: Time Complexity, Inferential Uncertainty, and\nSpacekime Analytics\", De Gruyter STEM Series, ISBN\n978-3-11-069780-3.\n. The package\nincludes 18 core functions which can be separated into three\ngroups. 1) draw longitudinal data, such as Functional magnetic\nresonance imaging(fMRI) time-series, and forecast or transform\nthe time-series data. 2) simulate real-valued time-series data,\ne.g., fMRI time-courses, detect the activated areas, report the\ncorresponding p-values, and visualize the p-values in the 3D\nbrain space. 3) Laplace transform and kimesurface\nreconstructions of the fMRI data.", "License": "GPL-3", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.3", "NeedsCompilation": "yes", "SystemRequirements": "GNU make", "Packaged": { "Date": "2026-05-26 06:36:47 UTC", "User": "root" }, "Author": "Yongkai Qiu [aut], Zhe Yin [aut], Jinwen Cao [aut], Yupeng\nZhang [aut], Yuyao Liu [aut], Rongqian Zhang [aut], Yueyang\nShen [aut, cre], Rouben Rostamian [ctb], Ranjan Maitra [ctb],\nDaniel Rowe [ctb], Daniel Adrian [ctb] (gLRT method for\ncomplex-valued fMRI statistics), Yunjie Guo [aut], Ivo Dinov\n[aut]", "Maintainer": "Yueyang Shen ", "Config/pak/sysreqs": "cmake make libgsl0-dev libicu-dev libuv1-dev\nlibssl-dev", "Repository": "https://petersyy1677.r-universe.dev", "Date/Publication": "2026-01-26 04:35:06 UTC", "RemoteUrl": "https://github.com/cran/TCIU", "RemoteRef": "HEAD", "RemoteSha": "bf50a78f3fd95c68e11aa9cd252c84eec92bd8cc", "MD5sum": "16d922f0b2d2dcdb32ba34a756ca8f0e", "_user": "petersyy1677", "_type": "src", "_file": "TCIU_1.2.8.tar.gz", "_fileid": "7d5bab70389f975444e082bb6afa2d45559060cdac347f11f2127c5ec659ee6d", "_filesize": 1796644, "_sha256": "7d5bab70389f975444e082bb6afa2d45559060cdac347f11f2127c5ec659ee6d", "_created": "2026-05-26T06:36:47.000Z", "_published": "2026-05-26T06:43:29.313Z", "_distro": "noble", "_jobs": [ { "job": 77820519787, "time": 251, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7210479578" }, { "job": 77820519752, "time": 236, "config": "linux-devel-x86_64", "r": "4.7.0", 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