FunChisq (2.4.5-1)

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.

Maintainer: Joe Song
Author(s): Yang Zhang [aut], Hua Zhong [aut], Ruby Sharma [aut], Sajal Kumar [aut], Joe Song [aut, cre]

License: LGPL (>= 3)

Uses: Rcpp, testthat, Ckmeans.1d.dp, knitr, rmarkdown
Reverse suggests: DiffXTables, GridOnClusters

Released almost 2 years ago.