Efficient simulation-based power and sample size calculations are supported for a
  broad class of late-stage clinical trials. The following modules are included in
  the package:
  Adaptive designs with data-driven sample size or event count re-estimation,
  Adaptive designs with data-driven treatment selection,
  Adaptive designs with data-driven population selection,
  Optimal selection of a futility stopping rule,
  Event prediction in event-driven trials,
  Adaptive trials with response-adaptive randomization (experimental module),
  Traditional trials with multiple objectives (experimental module).
  Traditional trials with cluster-randomized designs (experimental module).

Extract textual data from different media channels 
    through its source based on users choice of keywords. 
    These data can be used to perform text analysis to 
    identify patterns in respective media reporting.
    The media channels used in this package are print media.
    The data (or news) used are publicly available to consumers.

Functions for calculating the point and interval estimates of the natural indirect effect (NIE), total effect (TE), and mediation proportion (MP), based on the product approach. We perform the methods considered in Cheng, Spiegelman, and Li (2021) Estimating the natural indirect effect and the mediation proportion via the product method.

We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, and Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.

Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results 
 to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. 
 First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as 
 sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the 
 strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured 
 pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in 
 different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based 
 methods. It is applicable to both randomized experiments and observational studies.