FunChisq: 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) <doi:10.48550/arXiv.1311.2707>,
 an adapted functional chi-squared test (Kumar & Song 2022) 
 <doi:10.1093/bioinformatics/btac206>, 
 and an exact functional test (Zhong & Song 2019)
 <doi:10.1109/TCBB.2018.2809743> (Nguyen et al. 2020)
 <doi:10.24963/ijcai.2020/372>. 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) <doi:10.1038/nmeth.3773>. A
 function index (Zhong & Song 2019)
 <doi:10.1186/s12920-019-0565-9> (Kumar et al. 2018)
 <doi:10.1109/BIBM.2018.8621502> 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.
| Version: | 2.5.4 | 
| Depends: | R (≥ 3.0.0) | 
| Imports: | Rcpp, Rdpack (≥ 0.6-1), stats, dqrng | 
| LinkingTo: | BH, Rcpp | 
| Suggests: | Ckmeans.1d.dp, DescTools, DiffXTables, GridOnClusters, infotheo, knitr, rmarkdown, testthat (≥ 3.0.0) | 
| Published: | 2024-05-10 | 
| DOI: | 10.32614/CRAN.package.FunChisq | 
| Author: | Yang Zhang [aut],
  Hua Zhong  [aut],
  Hien Nguyen  [aut],
  Ruby Sharma  [aut],
  Sajal Kumar  [aut],
  Yiyi Li  [aut],
  Joe Song  [aut,
    cre] | 
| Maintainer: | Joe Song  <joemsong at cs.nmsu.edu> | 
| License: | LGPL (≥ 3) | 
| URL: | https://www.cs.nmsu.edu/~joemsong/publications/ | 
| NeedsCompilation: | yes | 
| Citation: | FunChisq citation info | 
| Materials: | README, NEWS | 
| CRAN checks: | FunChisq results | 
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