FunChisq (

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Model-Free Functional Chi-Squared and Exact Tests.

Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics 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-squared tests ('Zhang & Song' 2013) and an exact functional test ('Zhong & Song' 2019) . The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges ('Hill et al' 2016) . A function index ('Zhong & Song' in press) ('Kumar et al' 2018) derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. 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-squared or Fisher's exact tests.

Maintainer: Joe Song
Author(s): Yang Zhang [aut], Hua Zhong [aut] (<>), Hien Nguyen [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

Released 5 months ago.

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