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Using a common null\npopulation following a uniform distribution, contingency tables\nare comparable by statistical significance---not the case for\nmost association tests defining a varying null population by\ntensor product of observed marginals. Although Pearson's\nchi-squared test, Fisher's exact test, and Woolf's G-test\n(related to mutual information) are useful in some contexts,\nthe Upsilon test appeals to ranking association patterns not\nnecessarily following same marginal distributions, such as in\ncount data from DNA and RNA sequencing---a rapidly expanding\nfrontier in modern science.", "Encoding": "UTF-8", "License": "LGPL (>= 3)", "RdMacros": "Rdpack", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-05 06:59:04 UTC", "User": "root" }, "Author": "Xuye Luo [aut], Joe Song [aut, cre] (ORCID:\n)", "Maintainer": "Joe Song ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-03-07 13:20:08 UTC", "RemoteUrl": "https://github.com/cran/Upsilon", "RemoteRef": "HEAD", "RemoteSha": "3bba8771cf6c6a06f26f811a0d24af33a724c8ef", "MD5sum": "cafa033684f85ffce6e0a1e568c13909", "_user": "cran", "_type": "src", "_file": 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