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eshrink

Shrinkage estimators for estimating regression parameters

This R package provides functions for estimating the penalization parameter for shrinkage estimators using the approach of Keller and Rice (2017). The package currently contains functionality for ridge regressiona and the LASSO. The penalty parameter is selected by minimizing bias and/or variance in data generated from the posterior predictive distribution.

References

Keller JP and Rice KM. (2017) Selecting Shrinkage Parameters for Effect Estimation: the Multi-Ethnic Study of Atherosclerosis. American Journal of Epidemiology. https://doi.org/10.1093/aje/kwx225

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Version

Install

install.packages('eshrink')

Monthly Downloads

169

Version

0.2.0

License

GPL (>= 2)

Maintainer

Kayleigh Keller

Last Published

September 26th, 2025

Functions in eshrink (0.2.0)

simLASSO

Compute Lasso Estimator for simulated Data
vcovfestRidge

Standard Error Estimate
eshrink-package

Shrinkage Estimators for Regression
samplePosterior

Posterior Sample for Bayesian Linear Regression
festLASSO

Compute `Future Loss' Ridge or LASSO Estimates
estRidge

Estimate Coefficients for Ridge Regression
mseRidge

Compute MSE, Bias, and Variance for Ridge Estimator
check_CIbound

Confidence intervals for 'fLoss' estimators