CIMTx
Causal Inference for Multiple Treatments with a Binary Outcome
Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. doi:10.1177/0962280220921909.
- Version1.2.0
- R versionunknown
- LicenseMIT
- Needs compilation?No
- Last release06/24/2022
Team
Jiayi Ji
Liangyuan Hu
Show author detailsRolesAuthorChenyang Gu
Show author detailsRolesAuthorMichael Lopez
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 278 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 8 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 3,419 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 37 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports18 packages