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 489 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 14 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 5,576 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do 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.