⚠️There's a newer version (2.3) of this package.Take me there.
RXshrink (version 1.4.3)
Maximum Likelihood Shrinkage using Generalized Ridge or Least
Angle Regression Methods
Description
Functions are provided to calculate and display ridge TRACE diagnostics for
a variety of shrinkage Paths. TRACEs identify the m-Extent of shrinkage most likely,
under Normal-theory, to produce optimally biased estimates of beta-coefficients with
minimum MSE Risk. The unr.ridge() function implements the "Unrestricted Path"
introduced in Obenchain (2020) . This Shrinkage-Path is more
efficient than the Paths used by the qm.ridge(), aug.lars() and uc.lars() functions.
Optimally biased predictions can be made using RXpredict() for all six types of
RXshrink linear model estimation methods. Functions MLboot(), MLcalc(), MLhist() and
MLtrue() provide insights into the true bias and MSE risk characteristics of non-linear
Shrinkage estimators. Functions unr.aug() and unr.biv() augment the calculations made
by unr.ridge() to provide plots of the bivariate confidence ellipses corresponding to
any of the p*(p-1) possible pairs of shrunken regression coefficients. The correct.signs()
function provides estimates with "correct" numerical signs when ill-conditioned (nearly
multicollinear) models yield OLS estimates that disagree with the signs of the observed
correlations between the y-outcome and the selected x-predictor variables.