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.
|Author||Yang Zhang [aut], Hua Zhong [aut], Ruby Sharma [aut], Sajal Kumar [aut], Joe Song [aut, cre]|
|Date of publication||2017-02-28 10:58:55|
|Maintainer||Joe Song <firstname.lastname@example.org>|
|License||LGPL (>= 3)|
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