Estimates average treatment effects using model average double robust (MA-DR) estimation. The MA-DR estimator is defined as weighted average of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA-DR estimator extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models.

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.

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.