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  "Title": "Adaptable Regularized Hotelling's T^2 Test for High-Dimensional\nData",
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  "Authors@R": "person(\"Haoran\", \"Li\", email = \"hrli@ucdavis.edu\", role = c(\"aut\", \"cre\"))",
  "Description": "Perform the Adaptable Regularized Hotelling's T^2 test\n(ARHT) proposed by Li et al., (2016) <arXiv:1609.08725>. Both\none-sample and two-sample mean test are available with various\nprobabilistic alternative prior models. It contains a function\nto consistently estimate higher order moments of the population\ncovariance spectral distribution using the spectral of the\nsample covariance matrix (Bai et al. (2010)\n<doi:10.1111/j.1467-842X.2010.00590.x>). In addition, it\ncontains a function to sample from 3-variate chi-squared random\nvectors approximately with a given correlation matrix when the\ndegrees of freedom are large.",
  "License": "GPL (>= 2)",
  "Encoding": "UTF-8",
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  "Repository": "https://haoranli.r-universe.dev",
  "Date/Publication": "2018-04-10 22:42:52 UTC",
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    "Date": "2026-05-25 09:41:41 UTC",
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