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  "Title": "Correlation Estimation for Exchangeable/Symmetrical Variables",
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  "Authors@R": "c(\nperson(given = \"Alejandro\",\nfamily = \"Ochoa\",\nrole = c(\"aut\", \"cre\"),\nemail = \"alejandro.ochoa@duke.edu\",\ncomment = c(ORCID = \"0000-0003-4928-3403\")),\nperson(given = \"Gabriel\",\nfamily = \"Kennedy\",\nrole = c(\"aut\"),\nemail = \"gabriel.kennedy@duke.edu\",\ncomment = c(ORCID = \"0000-0003-4412-3777\"))\n)",
  "Description": "We implement a new correlation estimator, CorSym, designed\nfor exchangeable variables, where the ordering of the two\nvalues in the pair is arbitrary.  This kind of data arises\nfrequently in the study of assortative pairing (for example,\nincome in a couple).  The standard Pearson estimator is\nsensitive to such ordering and can be highly biased when the\norder is biased (when the first value tends to have lower or\nhigher values than the second value).  CorSym gives the same\nestimate deterministically for all orders within each pair, and\nestimates the desired correlation without bias (variables must\nbe exchangeable).  The package also includes utilities to\nsimulate biased orders and test for order bias.  Described in\nKennedy and Ochoa (2026) <doi:10.64898/2026.08.22.746446>.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "Config/testthat/edition": "3",
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  "URL": "https://github.com/OchoaLab/corsym",
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  "Repository": "https://ochoalab.r-universe.dev",
  "Date/Publication": "2026-09-09 13:12:38 UTC",
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    "User": "root"
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  "Author": "Alejandro Ochoa [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4928-3403>),\nGabriel Kennedy [aut] (ORCID: <https://orcid.org/0000-0003-4412-3777>)",
  "Maintainer": "Alejandro Ochoa <alejandro.ochoa@duke.edu>",
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  "_created": "2026-09-18T11:49:28.000Z",
  "_published": "2026-09-18T11:53:43.109Z",
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    "login": "alexviiia",
    "orcid": "0000-0003-4928-3403",
    "description": "Statistical geneticist, computational biologist.  Well-rounded nerd, also love languages and music.",
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      "week": "2026-32",
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    "corsym",
    "order_bias_test",
    "partial_order",
    "pearson",
    "randomize_order"
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      "page": "add_cor_CI",
      "title": "Calculate confidence intervals (CIs) for correlation estimates",
      "topics": [
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    {
      "page": "corsym",
      "title": "Calculate CorSym estimates with confidence intervals (CIs)",
      "topics": [
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      "title": "Test for order bias",
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      "page": "partial_order",
      "title": "Apply partial ordering to the rows of a matrix",
      "topics": [
        "partial_order"
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    {
      "page": "pearson",
      "title": "Calculate Pearson estimates with confidence intervals (CIs)",
      "topics": [
        "pearson"
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      "page": "randomize_order",
      "title": "Randomize ordering of each row of a matrix",
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      "title": "CorSym: Correlation Estimation For Exchangeable/Symmetrical Variables",
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      "headings": [
        "When to use CorSym",
        "Simulated example: extreme ordering",
        "Simulated example: partial orderings"
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      "created": "2026-08-06 21:04:21",
      "modified": "2026-08-26 23:12:39",
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