shrinkr: Modular Bayesian Hierarchical Shrinkage Models

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.

Version:	0.4.5
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
Published:	2026-07-06
DOI:	10.32614/CRAN.package.shrinkr
Author:	Jacob M. Maronge [aut, cre], GlaxoSmithKline Research & Development Limited [cph, fnd], Trustees of Columbia University [cph] (R/stanmodels.R, configure, configure.win)
Maintainer:	Jacob M. Maronge <jacob.m.maronge at gsk.com>
BugReports:	https://github.com/GSK-Biostatistics/shrinkr/issues
License:	GPL (≥ 3)
URL:	https://gsk-biostatistics.github.io/shrinkr/
NeedsCompilation:	yes
SystemRequirements:	GNU make, C++17 compiler
Materials:	README, NEWS
CRAN checks:	shrinkr results

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