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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

129

Version

0.1.2

License

GPL (>= 2)

Maintainer

Joshua Keller

Last Published

September 9th, 2020

Functions in eshrink (0.1.2)

check_CIbound

Confidence intervals for 'fLoss' estimators
estRidge

Estimate Coefficients for Ridge Regression
samplePosterior

Posterior Sample for Bayesian Linear Regression
mseRidge

Compute MSE, Bias, and Variance for Ridge Estimator
festLASSO

Compute `Future Loss' Ridge or LASSO Estimates
simLASSO

Compute Lasso Estimator for simulated Data
vcovfestRidge

Standard Error Estimate
eshrink-package

Shrinkage Estimators for Regression