Provides a means to style plots through cascading style sheets.
    This separates the aesthetics from the data crunching in plots and charts.

Assists in the whole process of designing and evaluating Randomized Control Trials.
    Robust treatment assignment by strata/blocks, that handles misfits; 
    Power calculations of the minimum detectable treatment effect or minimum populations;
    Balance tables of T-test of covariates; 
    Balance Regression: (treatment ~ all x variables) with F-test of null model; 
    Impact_evaluation: Impact evaluation regressions. This function
    gives you the option to include control_vars, fixed effect variables,
    cluster variables (for robust SE), multiple endogenous variables and
    multiple heterogeneous variables (to test treatment effect heterogeneity)
    summary_statistics: Function that creates a summary statistics table with statistics 
    rank observations in n groups: Creates a factor variable with n groups. Each group has 
    a min and max label attach to each category.
    Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.

Provides various statistical methods for designing and analyzing two-stage randomized controlled 
	trials using the methods developed by Imai, Jiang, and Malani (2021) <doi:10.1080/01621459.2020.1775612> and 
	(2022+) <doi:10.48550/arXiv.2011.07677>.  The package enables the estimation of direct and spillover effects, 
	conduct hypotheses tests, and conduct sample size calculation for two-stage randomized controlled trials.

Predict fish year-class strength by calibration
  regression analysis of multiple recruitment index series.

Accurate prediction of subject recruitment for Randomized Clinical
    Trials (RCT) remains an ongoing challenge. Many previous prediction models rely
    on parametric assumptions. We present functions for non-parametric RCT
    recruitment prediction under several scenarios.

Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v2>.