Hypothesis Testing in Cluster-Randomized Encouragement Designs
Two-sided test for the average cluster effect ratio estimand
Two-sided double-rank test for Fisher's sharp null hypothesis in a clu...
Construct a two-sided confidence interval for the proportional treatme...
Two-sided test for the pooled effect ratio estimand
Construct a two-sided confidence interval for the pooled effect ratio
An implementation of randomization-based hypothesis testing for three different estimands in a cluster-randomized encouragement experiment. The three estimands include (1) testing a cluster-level constant proportional treatment effect (Fisher's sharp null hypothesis), (2) pooled effect ratio, and (3) average cluster effect ratio. To test the third estimand, user needs to install 'Gurobi' (>= 9.0.1) optimizer via its R API. Please refer to <https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html>.