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  "Title": "Working with and Analyzing Functional Data of Varying Lengths",
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  "Authors@R": "c(\nperson(given = \"Pavel\", family = \"Hernandez\", role = c(\"aut\", \"cre\"),\nemail = \"pavel.amaro96@gmail.com\"),\nperson(given = \"Jose Ignacio\", family = \"Diez\", role = c(\"ctr\"),\nemail = \"jodiezru@pa.uc3m.es\"),\nperson(given = \"Maria\", family = \"Durban\", role = c(\"ctb\"),\nemail = \"mdurban@est-econ.uc3m.es\"),\nperson(given = \"Maria del Carmen\", family = \"Aguilera-Morillo\", role = c(\"ctb\"),\nemail = \"mdagumor@eio.upv.es\")\n)",
  "Description": "Comprehensive set of tools for analyzing and manipulating\nfunctional data with non-uniform lengths. This package\naddresses two common scenarios in functional data analysis:\nVariable Domain Data, where the observation domain differs\nacross samples, and Partially Observed Data, where observations\nare incomplete over the domain of interest. 'VDPO' enhances the\nflexibility and applicability of functional data analysis in\n'R'. See Amaro et al. (2024) <doi:10.48550/arXiv.2401.05839>,\nHernandez-Amaro et al. (2025) <doi:10.48550/arXiv.2510.26917>,\nand Hernandez-Amaro et al. (2026)\n<doi:10.48550/arXiv.2605.03633>.",
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  "Encoding": "UTF-8",
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    "ffpo_2d",
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      "topics": [
        "ffpo"
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      "title": "Multivariate functional principal component analysis for variable domain data",
      "topics": [
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      "title": "Plot Functional Curves with Confidence Intervals",
      "topics": [
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      ]
    },
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