R Package for Designing and Analyzing Randomized Experiments
Bounding the Average Treatment Effect when some of the Outcome Data ar...
Estimation of the Average Treatment Effects in Cluster-Randomized Expe...
Estimation of the Average Treatment Effect in Randomized Experiments
Bounding the ATOP when some of the Outcome Data are Missing Under the ...
Sensitivity analysis for the ATOP when some of the Outcome Data are Mi...
Sensitivity analysis for the ATOP when some of the Outcome Data are Mi...
Estimation of the unnormalized Area Under Prescription Evaluation Curv...
Estimation of the Complier Average Causal Effects in Cluster-Randomize...
Randomization-based method for the complier average direct effect and ...
Regression-based method for the complier average direct effect
Bayesian Analysis of Randomized Experiments with Noncompliance and Mis...
Estimation of the Population Average Prescription Difference in Comple...
Estimation of the Population Average Prescription Effect in Completely...
Randomization of the Treatment Assignment for Conducting Experiments
Provides various statistical methods for designing and analyzing randomized experiments. One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data.
Useful links