Modular Bayesian Hierarchical Shrinkage Models R package shrinkr version 0.4.5 Implements a two-stage Bayesian hierarchical modeling framework for applying shrinkage to subgroup-specific effects. The package separates model fitting (Stage 1) from hierarchical shrinkage (Stage 2), enabling modular sensitivity analyses without refitting expensive Markov chain Monte Carlo (MCMC) chains. Supports flexible prior specifications through the 'distributional' package, mixture approximations via 'mclust', and efficient 'Stan'-based inference. Software Depends: R (>= 4.1) Imports: rstan (>= 2.32.7), mclust, posterior, distributional, cli, tibble, dplyr, purrr, stats, utils, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.5.0) LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.32.7), StanHeaders (>= 2.32.10) Suggests: testthat (>= 3.0.0), ggplot2, knitr, rmarkdown, patchwork, tidybayes, bayesplot, tidyverse, ggdist, ggridges, brms, tidyr, survival, MASS, Matrix, beastt Jacob M. Maronge <jacob.m.maronge@gsk.com> Comprehensive R Archive Network (CRAN) Jacob M. Maronge [aut, cre] (ORCID: <https://orcid.org/0000-0003-3606-0841>), GlaxoSmithKline Research & Development Limited [cph, fnd], Trustees of Columbia University [cph] (R/stanmodels.R, configure, configure.win) GPL (>= 3) 2026-07-06 application/tgz https://CRAN.R-project.org/package=shrinkr doi:10.32614/CRAN.package.shrinkr