{ "_id": "6ac4f8a0f511ae58c09a3a36", "Package": "FunChisq", "Type": "Package", "Version": "2.5.4", "Date": "2024-05-10", "Title": "Model-Free Functional Chi-Squared and Exact Tests", "Authors@R": "c(\nperson(\"Yang\", \"Zhang\", role = \"aut\"),\nperson(\"Hua\", \"Zhong\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-1962-2603\")),\nperson(\"Hien\", \"Nguyen\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-7237-4752\")),\nperson(\"Ruby\", \"Sharma\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-7774-4065\")),\nperson(\"Sajal\", \"Kumar\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-0930-1582\")),\nperson(\"Yiyi\", \"Li\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-8859-3987\")),\nperson(\"Joe\", \"Song\", role = c(\"aut\", \"cre\"),\nemail = \"joemsong@cs.nmsu.edu\",\ncomment = c(ORCID = \"0000-0002-6883-6547\")))", "Author": "Yang Zhang [aut], Hua Zhong [aut]\n(), Hien Nguyen [aut]\n(), Ruby Sharma [aut]\n(), Sajal Kumar [aut]\n(), Yiyi Li [aut]\n(), Joe Song [aut, cre]\n()", "Maintainer": "Joe Song ", "Description": "Statistical hypothesis testing methods for inferring\nmodel-free functional dependency using asymptotic chi-squared\nor exact distributions. Functional test statistics are\nasymmetric and functionally optimal, unique from other related\nstatistics. Tests in this package reveal evidence for causality\nbased on the causality-by- functionality principle. They\ninclude asymptotic functional chi-squared tests (Zhang & Song\n2013) , an adapted functional\nchi-squared test (Kumar & Song 2022)\n, and an exact functional\ntest (Zhong & Song 2019) \n(Nguyen et al. 2020) . The\nnormalized functional chi-squared test was used by Best\nPerformer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer\nNetwork Inference Challenges (Hill et al. 2016)\n. A function index (Zhong & Song 2019)\n (Kumar et al. 2018)\n derived from the functional\ntest statistic offers a new effect size measure for the\nstrength of functional dependency, a better alternative to\nconditional entropy in many aspects. For continuous data, these\ntests offer an advantage over regression analysis when a\nparametric functional form cannot be assumed; for categorical\ndata, they provide a novel means to assess directional\ndependency not possible with symmetrical Pearson's chi-squared\nor Fisher's exact tests.", "License": "LGPL (>= 3)", "Encoding": "UTF-8", "RdMacros": "Rdpack", "NeedsCompilation": "yes", "URL": "https://www.cs.nmsu.edu/~joemsong/publications/", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "Packaged": { "Date": "2026-10-06 13:25:21 UTC", "User": "root" }, "Repository": "https://joemsong.r-universe.dev", "Date/Publication": "2024-05-11 02:33:53 UTC", "RemoteUrl": "https://github.com/cran/FunChisq", "RemoteRef": "HEAD", "RemoteSha": "90eb2fc97bc3584be02471ecf12d73d427952447", "_user": "joemsong", "_type": "src", "_file": "FunChisq_2.5.4.tar.gz", "_fileid": "https://r2.ropensci.org/8f3c054e2fde838e15d10a8ece9dcba9cb99c433b8c756fecec87c61654eac37", "_filesize": 845198, 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