Provides a framework which should improve reproducibility and
    transparency in data processing. It provides functionality such as
    automatic meta data creation and management, rudimentary quality
    management, data caching, work-flow management and data aggregation.
    * The title is a wish not a promise. By no means we expect this
    package to deliver everything what is needed to achieve full
    reproducibility and transparency, but we believe that it supports
    efforts in this direction.

Functions to support data cleaning, evaluation, and description, 
     developed for integration with Maelstrom Research software tools. 'madshapR' 
     provides functions primarily to evaluate and manipulate datasets and 
     data dictionaries in preparation for data harmonization with the package 
     'Rmonize' and to facilitate integration and transfer between RStudio servers 
     and secure Opal environments. 'madshapR' functions can be used independently
     but are optimized in conjunction with ‘Rmonize’ functions for streamlined 
     and coherent harmonization processing.

This function allows to generate two biological conditions synthetic 
             microarray dataset which has similar behavior to those currently 
             observed with common platforms. User provides a subset of parameters.
             Available default parameters settings can be modified.

Conducts one- and two-sample hypothesis tests for median
    absolute deviations (mads) for robust inference of dispersion.
    Comparisons between two samples uses the ratio of mads.  Confidence
    intervals are also computed.

Framework for creating and orchestrating data pipelines. Organize, orchestrate, and monitor multiple pipelines in a single project. Use tags to decorate functions with scheduling parameters and configuration.

A bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.