Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. This methodology based on Psioda et al (2025) <doi:10.1080/10543406.2025.2489285>.
Version: 0.0.3 Depends: R (≥ 4.1.0) Imports: cli, cobalt, distributional, dplyr, generics, ggdist, ggplot2, methods, mixtools, purrr, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rlang, rstan (≥ 2.18.1), rstantools (≥ 2.4.0), stringr, tidyr LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0) Suggests: knitr, mvtnorm, rmarkdown, spelling, testthat (≥ 3.0.0), tibble, vdiffr, survival Published: 2025-05-15 DOI: 10.32614/CRAN.package.beastt Author: Christina Fillmore [aut, cre], Nate Bean [aut], Abi Terry [aut], Ben Arancibia [aut], GlaxoSmithKline Research & Development Limited [cph, fnd], Trustees of Columbia University [cph] (R/stanmodels.R, configure, configure.win) Maintainer: Christina Fillmore <christina.e.fillmore at gsk.com> BugReports: https://github.com/GSK-Biostatistics/beastt/issues License: GPL (≥ 3) URL: https://gsk-biostatistics.github.io/beastt/, https://github.com/GSK-Biostatistics/beastt NeedsCompilation: yes SystemRequirements: GNU make Language: en-US Materials: README NEWS CRAN checks: beastt results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=beastt to link to this page.
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