Description: Statistical hypothesis testing methods for non-parametric functional dependencies using asymptotic chi-square or exact statistics. These tests were motivated to reveal evidence for causality based on functional dependencies. 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 symmetric Pearson's chi-square or Fisher's exact tests.

Description: Statistical hypothesis testing methods for non-parametric functional dependencies using asymptotic chi-square or exact statistics. These tests were motivated to reveal evidence for causality based on functional dependencies. 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.

Authors@R: c(person("Yang", "Zhang", role = "aut"),
Author: Yang Zhang [aut], Hua Zhong [aut], Ruby Sharma [aut], Sajal Kumar [aut], Joe Song [aut, cre]
Description: 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