rbmi

Implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) doi:10.21105/joss.04251). In particular, this package supports deterministic conditional mean imputation and jackknifing as described in Wolbers et al. (2022) doi:10.1002/pst.2234, Bayesian multiple imputation as described in Carpenter et al. (2013) doi:10.1080/10543406.2013.834911, and bootstrapped maximum likelihood imputation as described in von Hippel and Bartlett (2021) doi:10.1214/20-STS793.

Last 30 days

This package has been downloaded 539 times in the last 30 days. More downloads than an obscure whitepaper, but not enough to bring down any servers. A solid effort! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 24 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 5,573 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Feb 20, 2025 with 91 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.