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  "Title": "Non-Parametric Cluster Significance Testing with Reference to a\nUnimodal Null Distribution",
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  "Authors@R": "c(person(given = c(\"Erika\", \"S.\"),\nfamily = \"Helgeson\",\nrole = c(\"aut\", \"cre\"),\nemail = \"helge@umn.edu\"),\nperson(given = \"David\",\nfamily = \"Vock\",\nrole = \"aut\"),\nperson(given = \"Eric\",\nfamily = \"Bair\",\nrole = \"aut\"))",
  "Description": "Assess the significance of identified clusters and\nestimates the true number of clusters by comparing the\nexplained variation due to the clustering from the original\ndata to that produced by clustering a unimodal reference\ndistribution which preserves the covariance structure in the\ndata. The reference distribution is generated using kernel\ndensity estimation and a Gaussian copula framework. A dimension\nreduction strategy and sparse covariance estimation optimize\nthis method for the high-dimensional, low-sample size setting.\nThis method is described in Helgeson, Vock, and Bair (2021)\n<doi:10.1111/biom.13376>.",
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  "Author": "Erika S. Helgeson [aut, cre], David Vock [aut], Eric Bair [aut]",
  "Maintainer": "Erika S. Helgeson <helge@umn.edu>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-01-30 03:09:01 UTC",
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