A set of functions to perform Raju, van der Linden and Fleer's
(1995, <doi:10.1177/014662169501900405>) Differential Functioning of Items
and Tests (DFIT) analyses. It includes functions to use the Monte Carlo Item
Parameter Replication approach (Oshima, Raju, & Nanda, 2006, <doi:10.1111/j.1745-3984.2006.00001.x>)
for obtaining the associated statistical significance
tests cut-off points. They may also be used for a priori and post-hoc power
calculations (Cervantes, 2017, <doi:10.18637/jss.v076.i05>).

Performs differential correlation analysis on input
matrices, with multiple conditions specified by a design matrix. Contains
functions to filter, process, save, visualize, and interpret differential
correlations of identifier-pairs across the entire identifier space, or with
respect to a particular set of identifiers (e.g., one). Also contains several
functions to perform differential correlation analysis on clusters (i.e., modules)
or genes. Finally, it contains functions to generate empirical p-values for the
hypothesis tests and adjust them for multiple comparisons. Although the package
was built with gene expression data in mind, it is applicable to other types of
genomics data as well, in addition to being potentially applicable to data from
other fields entirely. It is described more fully in the manuscript
introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.
Bin Zhang [aut],
Andrew McKenzie [aut, cre]
knitr, impute, gplots, fdrtool, testthat, ggplot2, plotrix,
GOstats, HGNChelper, org.Hs.eg.db, AnnotationDbi, abind,
MEGENA, Matrix, doMC, igraph, cowplot, stats, utils

Differential Gene Expression (DGE) Analysis Results Data Object
Provides a flexible container to manage and annotate Differential Gene
Expression (DGE) analysis results (Smythe et. al (2015) <doi:10.1093/nar/gkv007>).
The DGEobj has data slots for row (gene), col (samples), assays (matrix n-rows
by m-samples dimensions) and metadata (not keyed to row, col, or assays).
A set of accessory functions to deposit, query and retrieve subsets of a data
workflow has been provided.  Attributes are used to capture metadata such as
species and gene model, including reproducibility information such that a 3rd
party can access a DGEobj history to see how each data object was created or
modified.  Since the DGEobj is customizable and extensible it is not limited
to RNA-seq analysis types of workflows -- it can accommodate nearly any data
analysis workflow that starts from a matrix of assays (rows) by samples (columns).
c(person("John", "Thompson", role = "aut", email = "jrt@thompsonclan.org"),
person("Connie", "Brett",  role = c("aut", "cre"), email = "connie@aggregate-genius.com"),
person("Isaac", "Neuhaus", role = "aut", email = "isaac.neuhaus@bms.com"),
person("Ryan", "Thompson", role = "aut", email = "rct@thompsonclan.org"))
assertthat, magrittr, stringr, utils, GenomicRanges
John Thompson [aut],
Connie Brett [aut, cre],
Isaac Neuhaus [aut],
Ryan Thompson [aut]