{
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  "Package": "KFPCA",
  "Title": "Kendall Functional Principal Component Analysis",
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  "Authors@R": "c(person(given = \"Rou\", family = \"Zhong\", role = c(\"aut\", \"cre\"),\nemail = \"zhong_rou@163.com\"),\nperson(given = \"Jingxiao\", family = \"Zhang\", role = \"aut\"))",
  "Description": "Implementation for Kendall functional principal component\nanalysis. Kendall functional principal component analysis is a\nrobust functional principal component analysis technique for\nnon-Gaussian functional/longitudinal data. The crucial function\nof this package is KFPCA() and KFPCA_reg(). Moreover, least\nsquare estimates of functional principal component scores are\nalso provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li,\nJingxiao Zhang. (2021) <arXiv:2102.01286>. Rou Zhong, Shishi\nLiu, Haocheng Li, Jingxiao Zhang. (2021)\n<doi:10.1016/j.jmva.2021.104864>.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-25 08:17:53 UTC",
    "User": "root"
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  "Author": "Rou Zhong [aut, cre], Jingxiao Zhang [aut]",
  "Maintainer": "Rou Zhong <zhong_rou@163.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2022-02-04 10:30:16 UTC",
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      "title": "CD4 cell counts",
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      "title": "Least square estimates of functional principal component scores",
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      "title": "Kendall Functional Principal Component Analysis (KFPCA) for sparse design",
      "topics": [
        "KFPCA"
      ]
    },
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      "title": "Kendall Functional Principal Component Analysis (KFPCA) for dense and regular design",
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      "page": "predict.KFPCA",
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