Title: Metrics of Difference for Comparing Pairs of Maps or Pairs of
Author: Robert Gilmore Pontius Jr. <rpontius@clarku.edu>, Ali Santacruz <asantacruzdelgado@clarku.edu>
Depends: R (>= 2.14.0), rgdal, raster, methods, ggplot2, reshape2
Description: Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.

Title: Computation of TES-Based Cell Differentiation Trees
Description: Computes the ATM (Attractor Transition Matrix) structure and
the tree-like structure describing the cell differentiation process
(based on the Threshold Ergodic Set concept introduced by Serra and
Villani), starting from the Boolean networks with synchronous updating
scheme of the 'BoolNet' R package. TESs (Threshold Ergodic Sets) are
the mathematical abstractions that represent the different cell types
arising during ontogenesis. TESs and the powerful model of biological
differentiation based on Boolean networks to which it belongs have
been firstly described in "A Dynamical Model of Genetic Networks for
Cell Differentiation" Villani M, Barbieri A, Serra R (2011) A
Dynamical Model of Genetic Networks for Cell Differentiation. PLOS ONE

Title: Fast and Scalable Learning of Sparse Changes in High-Dimensional
Authors@R: c(person("Beilun", "Wang", role = c("aut", "cre"), email = "bw4mw@virginia.edu"), person("Yanjun", "Qi", role = "aut", email = "yanjun@virginia.edu"))
Description: 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>.