Hypothesis Testing in Cluster-Randomized Encouragement Designs
Description
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
.