rbmi: Reference Based Multiple Imputation (original) (raw)
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>.
Version:
1.5.2
Depends:
R (≥ 3.4.0)
Imports:
mmrm, pkgload, Matrix, tools, methods, R6, assertthat, jinjar, fs, stringr
Suggests:
dplyr, tidyr, nlme, testthat, emmeans, tibble, mvtnorm, knitr, rmarkdown, bookdown, lubridate, purrr, ggplot2, rstan (≥ 2.26.0), R.rsp, withr
Published:
2025-10-28
DOI:
Author:
Craig Gower-Page [aut, cre], Isaac Gravestock [aut], Alessandro Noci [aut], Marcel Wolbers [ctb], Daniel Sabanes Bove [aut], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer:
Craig Gower-Page <craig.gower-page at novartis.com>
BugReports:
https://github.com/insightsengineering/rbmi/issues
License:
URL:
https://insightsengineering.github.io/rbmi/,https://github.com/insightsengineering/rbmi
NeedsCompilation:
no
Citation:
Materials:
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