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.

An interface to 'DifferentialEquations.jl' <https://diffeq.sciml.ai/dev/> from the R programming language.
  It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE),
  delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality,
  including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods.
  Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs).
  'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information,
  see Rackauckas and Nie (2017) <doi:10.5334/jors.151>.

Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.

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
    6(3): e17703.