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 https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html.
- Version0.1.0
- R version≥ 2.10
- LicenseGPL-3
- Needs compilation?No
- Last release07/14/2020
Team
Bo Zhang
Insights
Last 30 days
This package has been downloaded 117 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 9 times.
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Last 365 days
This package has been downloaded 1,523 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 22 downloads.
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Dependencies
- Suggests1 package