Package: FunChisq
Type: Package
Version: 2.5.2
Date: 2021-05-19
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("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>),
  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) <arXiv:1311.2707>,
 an adapted functional chi-squared test, 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
NeedsCompilation: yes
URL: https://www.cs.nmsu.edu/~joemsong/publications/
VignetteBuilder: knitr
Packaged: 2021-05-19 20:40:19 UTC; joesong
Repository: CRAN
Date/Publication: 2021-05-19 21:00:02 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-21 04:42:44 UTC; windows
Archs: i386, x64
