shrinkr: Modular Bayesian Hierarchical Shrinkage Models

The shrinkr package provides a flexible framework for two-stage Bayesian hierarchical modeling. It enables post-hoc shrinkage of subgroup-specific posterior estimates from any Bayesian model, with support for diverse prior specifications and diagnostic tools.

Key Features

Two-Stage Workflow:

• Stage 1: Fit any Bayesian model without shrinkage

• Stage 2: Apply hierarchical shrinkage with flexible priors

Flexible Priors:

• Standard families (Normal, Student-t, Cauchy, Lognormal)

• Heavy-tailed (Inverse-Gamma, Half-Cauchy, Half-t)

• Bounded (Uniform)

• Mixture priors (spike-and-slab)

• Truncated distributions

Input Methods:

• Full posterior samples (via mixture approximation)

• Point estimates + variance/covariance

Main Functions

Core Workflow:

• fit_mixture(): Approximate Stage 1 posteriors with Gaussian mixture

• shrink(): Main user interface for hierarchical shrinkage

Prior Specification:

• prior_spike_slab(): Create spike-and-slab mixture prior

• prior_mixture(): Create custom mixture prior

• sample_prior_predictive(): Generate prior predictive samples for checking

Extraction & Visualization:

• extract_mu_tau(): Extract hyperparameter draws

• extract_theta(): Extract group-level draws

• summarise_mu_tau(): Summarize hyperparameters

• summarise_theta(): Summarize group-level estimates

• theta_contrasts(): Compute linear combinations of theta

• plot(): Visualize shrinkage effect and mixture approximation quality

Getting Started

See vignette("getting_started", package = "shrinkr") for a basic workflow, or vignette("brms_integration", package = "shrinkr") for a survival analysis example.

Use Cases

• Meta-analysis: Shrink study-specific effects

• Clinical trials: Borrow information across subgroups or historical controls

• Genomics: Regularize gene-specific effects

• Simulation studies: Compare shrinkage methods systematically

Package Options

• shrinkr.refresh: Controls Stan sampling progress output (default: 100)

References

Maronge, J. M. (2026). shrinkr: Modular Bayesian Hierarchical Shrinkage Models. R package version 0.4.3.

See also

• Stan: https://mc-stan.org/

• distributional package: https://pkg.mitchelloharawild.com/distributional/

Author

Maintainer: Jacob M. Maronge jacob.m.maronge@gsk.com (ORCID)

Authors:

• Jacob M. Maronge jacob.m.maronge@gsk.com (ORCID)

Other contributors:

• GlaxoSmithKline Research & Development Limited [copyright holder, funder]

• Trustees of Columbia University (R/stanmodels.R, configure, configure.win) [copyright holder]

Examples

if (FALSE) { # \dontrun{
# This example fits a Stan model, so it is not run during package checks.
library(shrinkr)
priors <- list(
  mu = distributional::dist_normal(0, 5),
  tau = distributional::dist_truncated(distributional::dist_student_t(3, 0, 1), lower = 0)
)
fit <- shrink(
  mle = c(0.0, 0.5, 1.0),
  var_matrix = c(0.25, 0.25, 0.25),
  hierarchical_priors = priors,
  iter = 1000, chains = 2, seed = 1
)
summary(fit)
} # }