Pattern Analysis Across Contingency Tables

Statistical hypothesis testing of pattern heterogeneity via differences in underlying distributions across multiple contingency tables. Five tests are included: the comparative chi-squared test (Song et al. 2014) (Zhang et al. 2015) , the Sharma-Song test (Sharma et al. 2021) , the heterogeneity test, the marginal-change test (Sharma et al. 2020) , and the strength test (Sharma et al. 2020) . Under the null hypothesis that row and column variables are statistically independent and joint distributions are equal, their test statistics all follow an asymptotically chi-squared distribution. A comprehensive type analysis categorizes the relation among the contingency tables into type null, 0, 1, and 2 (Sharma et al. 2020) . They can identify heterogeneous patterns that differ in either the first order (marginal) or the second order (differential departure from independence). Second-order differences reveal more fundamental changes than first-order differences across heterogeneous patterns.


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0.1.3 by Joe Song, a month ago

Browse source code at

Authors: Ruby Sharma [aut] , Joe Song [aut, cre]

Documentation:   PDF Manual  

LGPL (>= 3) license

Imports Rdpack

Depends on pander

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

Suggested by FunChisq.

See at CRAN