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 rigorous processes in data cleaning, 
     evaluation, and documentation across datasets from different studies based 
     on Maelstrom Research guidelines. The package includes the core functions 
     to evaluate and format the main inputs that define the process, diagnose 
     errors, and summarize and evaluate datasets and their associated 
     data dictionaries. The main outputs are clean datasets and associated 
     metadata, and tabular and visual summary reports. As described in 
     Maelstrom Research guidelines for rigorous retrospective data 
     harmonization (Fortier I and al. (2017) <doi:10.1093/ije/dyw075>).

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.