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>.

Compare functional enrichment between two experimentally-derived groups of genes or proteins (Peterson, DR., et al.(2018)) <doi: 10.1371/journal.pone.0198139>. Given a list of gene symbols, 'diffEnrich'  will
  perform differential enrichment analysis using the Kyoto Encyclopedia of Genes
  and Genomes (KEGG) REST API. This package provides a number of functions that are 
  intended to be used in a pipeline. Briefly, the user provides a KEGG formatted species id for either human, mouse or rat, and the package will
  download and clean species specific ENTREZ gene IDs and map them to their respective
  KEGG pathways by accessing KEGG's REST API. KEGG's API is used to guarantee the most up-to-date pathway data from KEGG. Next, the user will identify significantly
  enriched pathways from two gene sets, and finally, the user will identify 
  pathways that are differentially enriched between the two gene sets. In addition to 
  the analysis pipeline, this package also provides a plotting function.