Package: FunChisq
Type: Package
Version: 2.5.4
Date: 2024-05-10
Title: Model-Free Functional Chi-Squared and Exact Tests
Authors@R: c(
 person("Yang", "Zhang", role = "aut"),
 person("Hua", "Zhong", role = "aut",
	      comment = c(ORCID = "0000-0003-1962-2603")),
 person("Hien", "Nguyen", role = "aut",
	      comment = c(ORCID = "0000-0002-7237-4752")),
 person("Ruby", "Sharma", role = "aut",
        comment = c(ORCID = "0000-0001-7774-4065")),
 person("Sajal", "Kumar", role = "aut",
	      comment = c(ORCID = "0000-0003-0930-1582")),
 person("Yiyi", "Li", role = "aut", 
        comment = c(ORCID = "0000-0001-8859-3987")),
 person("Joe", "Song", role = c("aut", "cre"),
		    email = "joemsong@cs.nmsu.edu",
		    comment = c(ORCID = "0000-0002-6883-6547")))
Author: Yang Zhang [aut], Hua Zhong [aut]
        (<https://orcid.org/0000-0003-1962-2603>), Hien Nguyen [aut]
        (<https://orcid.org/0000-0002-7237-4752>), Ruby Sharma [aut]
        (<https://orcid.org/0000-0001-7774-4065>), Sajal Kumar [aut]
        (<https://orcid.org/0000-0003-0930-1582>), Yiyi Li [aut]
        (<https://orcid.org/0000-0001-8859-3987>), Joe Song [aut, cre]
        (<https://orcid.org/0000-0002-6883-6547>)
Maintainer: Joe Song <joemsong@cs.nmsu.edu>
Description: Statistical hypothesis testing methods for inferring
        model-free functional dependency using asymptotic chi-squared
        or exact distributions. Functional test statistics are
        asymmetric and functionally optimal, unique from other related
        statistics. Tests in this package reveal evidence for causality
        based on the causality-by- functionality principle. They
        include asymptotic functional chi-squared tests (Zhang & Song
        2013) <doi:10.48550/arXiv.1311.2707>, an adapted functional
        chi-squared test (Kumar & Song 2022)
        <doi:10.1093/bioinformatics/btac206>, and an exact functional
        test (Zhong & Song 2019) <doi:10.1109/TCBB.2018.2809743>
        (Nguyen et al. 2020) <doi:10.24963/ijcai.2020/372>. The
        normalized functional chi-squared test was used by Best
        Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer
        Network Inference Challenges (Hill et al. 2016)
        <doi:10.1038/nmeth.3773>. A function index (Zhong & Song 2019)
        <doi:10.1186/s12920-019-0565-9> (Kumar et al. 2018)
        <doi:10.1109/BIBM.2018.8621502> derived from the functional
        test statistic offers a new effect size measure for the
        strength of functional dependency, a better alternative to
        conditional entropy in many aspects. For continuous data, these
        tests offer an advantage over regression analysis when a
        parametric functional form cannot be assumed; for categorical
        data, they provide a novel means to assess directional
        dependency not possible with symmetrical Pearson's chi-squared
        or Fisher's exact tests.
License: LGPL (>= 3)
Encoding: UTF-8
Depends: R (>= 3.0.0)
Imports: Rcpp, Rdpack (>= 0.6-1), stats, dqrng
LinkingTo: BH, Rcpp
RdMacros: Rdpack
Suggests: Ckmeans.1d.dp, DescTools, DiffXTables, GridOnClusters,
        infotheo, knitr, rmarkdown, testthat (>= 3.0.0)
NeedsCompilation: yes
URL: https://www.cs.nmsu.edu/~joemsong/publications/
Config/testthat/edition: 3
VignetteBuilder: knitr
Packaged: 2026-10-06 13:25:21 UTC; 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