FunChisq: Chi-Square and Exact Tests for Model-Free Functional Dependency

Statistical hypothesis testing methods for model-free functional dependency using asymptotic chi-square or exact distributions. Functional chi-squares 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-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. 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-square or Fisher's exact tests.

Version: 2.4.5
Depends: R (≥ 3.0.0)
Imports: Rcpp, stats
LinkingTo: BH, Rcpp
Suggests: Ckmeans.1d.dp, testthat, knitr, rmarkdown
Published: 2018-02-20
Author: Yang Zhang [aut], Hua Zhong [aut], Ruby Sharma [aut], Sajal Kumar [aut], Joe Song [aut, cre]
Maintainer: Joe Song <joemsong at>
License: LGPL (≥ 3)
NeedsCompilation: yes
Citation: FunChisq citation info
Materials: NEWS
CRAN checks: FunChisq results


Reference manual: FunChisq.pdf
Vignettes: Using the exact functional test
Which quantity to measure functional dependency?
Package source: FunChisq_2.4.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: FunChisq_2.4.5.tgz
OS X Mavericks binaries: r-oldrel: FunChisq_2.4.3.tgz
Old sources: FunChisq archive


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