{
  "_id": "6a2007b3b401979e73446b9a",
  "Package": "OLCPM",
  "Type": "Package",
  "Title": "Online Change Point Detection for Matrix-Valued Time Series",
  "Version": "0.1.2",
  "Authors@R": "c(person(\"Yong\", \"He\", role=c(\"aut\")),\nperson(\"Xinbing\", \"Kong\", role=c(\"aut\")),\nperson(\"Lorenzo\", \"Trapani\", role=c(\"aut\")),\nperson(\"Long\", \"Yu\", role=c(\"aut\", \"cre\"), email = \"fduyulong@163.com\"))",
  "Author": "Yong He [aut], Xinbing Kong [aut], Lorenzo Trapani [aut], Long\nYu [aut, cre]",
  "Maintainer": "Long Yu <fduyulong@163.com>",
  "Description": "We provide two algorithms for monitoring change points\nwith online matrix-valued time series, under the assumption of\na two-way factor structure. The algorithms are based on\ndifferent calculations of the second moment matrices. One is\nbased on stacking the columns of matrix observations, while\nanother is by a more delicate projected approach. A well-known\nfact is that, in the presence of a change point, a factor model\ncan be rewritten as a model with a larger number of common\nfactors. In turn, this entails that, in the presence of a\nchange point, the number of spiked eigenvalues in the second\nmoment matrix of the data increases. Based on this, we propose\ntwo families of procedures - one based on the fluctuations of\npartial sums, and one based on extreme value theory - to\nmonitor whether the first non-spiked eigenvalue diverges after\na point in time in the monitoring horizon, thereby indicating\nthe presence of a change point. This package also provides some\nsimple functions for detecting and removing outliers, imputing\nmissing entries and testing moments. See more details in He et\nal. (2021)<doi:10.48550/arXiv.2112.13479>.",
  "License": "GPL-2 | GPL-3",
  "Encoding": "UTF-8",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-03 10:50:30 UTC",
    "User": "root"
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  "RoxygenNote": "7.3.1",
  "LazyData": "true",
  "Repository": "https://fduyulong.r-universe.dev",
  "Date/Publication": "2024-05-31 02:40:33 UTC",
  "RemoteUrl": "https://github.com/cran/OLCPM",
  "RemoteRef": "HEAD",
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  "MD5sum": "6d8b7b55a502b68e7cede45f2ee6c350",
  "_user": "fduyulong",
  "_type": "src",
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  "_created": "2026-06-03T10:50:30.000Z",
  "_published": "2026-06-03T10:53:39.078Z",
  "_distro": "noble",
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  "_buildurl": "https://github.com/r-universe/fduyulong/actions/runs/26879863930",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/cran/OLCPM",
  "_commit": {
    "id": "31f5b4d8b7834c812933ac3b0ba0ff13efd85471",
    "author": "Long Yu <fduyulong@163.com>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 0.1.2\n",
    "time": 1717123233
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      "title": "testing the number of row factors- with projection",
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      "topics": [
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    {
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      "page": "test.once.psi.robust",
      "title": "robust test of single change point for matrix-valued online data given rolling eigenvalue series",
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