Analyzing and Visualizing Differential Correlation Networks in
Biological Data
A method for identifying pattern changes between 2 experimental
conditions in correlation networks (e.g., gene co-expression networks),
which builds on a commonly used association measure, such as Pearson's
correlation coefficient. This package includes functions to calculate
correlation matrices for high-dimensional dataset and to test
differential correlation, which means the changes in the correlation
relationship among variables (e.g., genes and metabolites) between 2
experimental conditions.

c(person("Atsushi", "Fukushima", role = c("aut", "cre"),
email = "afukushima@gmail.com"),
person("Kozo", "Nishida", role = "aut",
email = "kozo.nishida@gmail.com"))
Atsushi Fukushima [aut, cre],
Kozo Nishida [aut]

Detection of Statistically Significant Changes in Complex
Biological Networks
Raghvendra Mall [aut, cre], Khalid B. Kunji [aut]
c(person(given = "Raghvendra", family = "Mall", role = c("aut",
"cre"), email = "rmall@hbku.edu.qa"), person(given = "Khalid B.",
family = "Kunji", role = c("aut"), email = "kkunji@hbku.edu.qa"))
Provides an implementation of statistically significant
differential sub-network analysis for paired biological networks.
Rcpp (>= 0.12.7), Matrix, qlcMatrix, data.table, Hmisc,
gplots, igraph, lsa, foreach, doParallel

Distributional Differences Across Contingency Tables
c(person(given = "Ruby",
family = "Sharma",
role = "aut",
email = "ruby49@nmsu.edu"),
person(given = "Joe",
family = "Song",
role = c("aut", "cre"),
email = "joemsong@cs.nmsu.edu",
comment = c(ORCID = "0000-0002-6883-6547")))
Ruby Sharma [aut],
Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>)
Statistical methods for hypothesis testing of differences in the
underlying distributions across two or more contingency tables. The package
includes three tests: 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, and the heterogeneity test. They all have an
asymptotically chi-squared null distribution. These options allow one to
test for patterns that differ in first and second orders, directly related
to marginal and joint distributions of given contingency tables.
Ckmeans.1d.dp, FunChisq, knitr, rmarkdown, testthat