Computations of Fisher's z-tests concerning different kinds of correlation differences. Additionally, approaches to estimating statistical power via Monte Carlo simulations are implemented. Important to note, the Pearson correlation coefficient is sensitive to linear association, but also to a host of statistical issues such as univariate and bivariate outliers, range restrictions, and heteroscedasticity (e.g., Duncan & Layard, 1973 <doi:10.1093/BIOMET/60.3.551>; Wilcox, 2013 <doi:10.1016/C2010-0-67044-1>). Thus, every power analysis requires that specific statistical prerequisites are fulfilled and can be invalid if the prerequisites do not hold.

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.

Functions for comparing two data.frames against 
    each other. The core functionality is to provide a detailed breakdown of any differences 
    between two data.frames as well as providing utility functions to help narrow down the 
    source of problems and differences.

Shows you which rows have changed between two data frames with the same column structure. Useful for diffing slowly mutating data.

This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.