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: 1.4.1 Depends: R (≥ 3.4.0) Imports: mmrm, pkgload, Matrix, tools, methods, R6, assertthat Suggests: dplyr, tidyr, nlme, testthat, emmeans, tibble, mvtnorm, knitr, rmarkdown, bookdown, lubridate, purrr, ggplot2, rstan (≥ 2.26.0), R.rsp Published: 2025-03-03 DOI: 10.32614/CRAN.package.rbmi Author: Isaac Gravestock [aut, cre], Craig Gower-Page [aut], Alessandro Noci [aut], Marcel Wolbers [ctb], Daniel Sabanes Bove [aut], F. Hoffmann-La Roche AG [cph, fnd] Maintainer: Isaac Gravestock <isaac.gravestock at roche.com> BugReports: https://github.com/insightsengineering/rbmi/issues License: Apache License (≥ 2) URL: https://insightsengineering.github.io/rbmi/, https://github.com/insightsengineering/rbmi NeedsCompilation: no Citation: rbmi citation info Materials: README NEWS In views: ClinicalTrials CRAN checks: rbmi results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=rbmi to link to this page.
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