FunChisq: Chi-Square and Exact Tests for Non-Parametric Functional Dependencies
Version 2.4.3

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

Package details

AuthorYang Zhang [aut], Hua Zhong [aut], Ruby Sharma [aut], Sajal Kumar [aut], Joe Song [aut, cre]
Date of publication2017-05-02 16:17:03 UTC
MaintainerJoe Song <joemsong@cs.nmsu.edu>
LicenseLGPL (>= 3)
Version2.4.3
URL https://www.cs.nmsu.edu/~joemsong/publications
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("FunChisq")

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FunChisq documentation built on May 29, 2017, 8:13 p.m.