ivdesign
Hypothesis Testing in Cluster-Randomized Encouragement Designs
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
- Version0.1.0
- R version≥ 2.10
- LicenseGPL-3
- Needs compilation?No
- Last release07/14/2020
Team
Bo Zhang
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package
- Imports1 package
- Suggests2 packages