FunChisq

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. A function index derived from the functional chi-square offers a new effect size measure for the strength of function 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-square or Fisher's exact tests.

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Description file content

Package
FunChisq
Type
Package
Version
2.4.5-3
Date
2018-12-06
Title
Chi-Square and Exact Tests for Model-Free Functional Dependency
Author
Yang Zhang [aut], Hua Zhong [aut], Ruby Sharma [aut], Sajal Kumar [aut], Joe Song [aut, cre]
Maintainer
Joe Song
Description
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. A function index derived from the functional chi-square offers a new effect size measure for the strength of function 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-square or Fisher's exact tests.
License
LGPL (>= 3)
Encoding
UTF-8
Depends
R (>= 3.0.0)
Imports
Rcpp, stats
LinkingTo
BH, Rcpp
Suggests
Ckmeans.1d.dp, testthat, knitr, rmarkdown
NeedsCompilation
yes
URL
LazyData
TRUE
VignetteBuilder
knitr
Packaged
2018-12-06 13:31:47 UTC; joemsong
Repository
CRAN
Date/Publication
2018-12-06 13:50:03 UTC

install.packages('FunChisq')

2.4.5-3

3 days ago

https://www.cs.nmsu.edu/~joemsong/publications

Joe Song

LGPL (>= 3)

Depends on

R (>= 3.0.0)

Imports

Rcpp, stats

Suggests

Ckmeans.1d.dp, testthat, knitr, rmarkdown

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