Title: Pattern Heterogeneity via Distributional Differences Across
Description: Statistical hypothesis testing of pattern heterogeneity via
differences in underlying distributions across two or more contingency
tables. Three tests are included: the comparative chi-squared test
<doi:10.1093/nar/gkv358>, the Sharma-Song test, and the heterogeneity test.
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. These options test for
heterogeneous patterns that differ in either the first order (marginal) or
the second order (joint distribution deviation from product of marginals).
Second-order differences may reveal more fundamental changes than
first-order differences across heterogeneous patterns.

Title: Pattern Analysis Across Contingency Tables
Author: Ruby Sharma [aut] (<https://orcid.org/0000-0001-7774-4065>),
Description: 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) <doi:10.1093/nar/gku086>
(Zhang et al. 2015) <doi:10.1093/nar/gkv358>, the Sharma-Song
test, 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 (joint
distribution deviation from product of marginals). Second-order
differences may reveal more fundamental changes than first-order
Suggests: Ckmeans.1d.dp, FunChisq, knitr, rmarkdown, testthat,