causalCmprsk
Nonparametric and Cox-Based Estimation of Average Treatment Effects in Competing Risks
Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) doi:10.1038/s41467-022-35157-w.
- Version2.0.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release07/04/2023
Documentation
Team
Bella Vakulenko-Lagun
Colin Magdamo
Show author detailsRolesAuthorMarie-Laure Charpignon
Show author detailsRolesAuthorBang Zheng
Show author detailsRolesAuthorMark Albers
Show author detailsRolesAuthorSudeshna Das
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 267 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 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 3,745 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 Jul 24, 2024 with 33 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.
Data provided by CRAN
Binaries
Dependencies
- Imports6 packages
- Suggests13 packages