Statistical hypothesis testing methods for non-parametric functional dependencies using asymptotic chi-square or exact statistics. These tests 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 dependencies not possible with symmetrical Pearson's chi-square or Fisher's exact tests.

Maintainer: Joe Song <joemsong at cs.nmsu.edu>

Author(s): Yang Zhang*, Hua Zhong*, Ruby Sharma*, Sajal Kumar*, Joe Song*

Install package and any missing dependencies by running this line in your R console:

install.packages("FunChisq")

Depends R (>= 3.0.0)
Imports Rcpp, stats
Suggests Ckmeans.1d.dp, testthat, knitr, rmarkdown
Enhances
Linking to BH, Rcpp
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Package FunChisq
Materials
URL https://www.cs.nmsu.edu/~joemsong/publications
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Version 2.4.0
Published 2017-02-28
License LGPL (>= 3)
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NeedsCompilation yes
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Package source FunChisq_2.4.0.tar.gz