# Boostrap Confidence Intervals for biased estimators in R?

So, this is a two part question. I have made a multilinear regression model in R. The model has been ridged with lm.ridge() with the $\lambda$ that minimizes the GCV prediction error. This was done by the following code:

# package{MASS}
models.ridge <- lm.ridge(formula, data = data, lambda = seq(0, 1.5, 1/100))

# Package{broom}
g <- glance(models.ridge)

# performs ridge with the "optimal" ridge constant obtained from g$lambdaGCV final.ridge <- lm.ridge(formula, data = data, lambda = g$lambdaGCV)


Now i want to bootstrap a confidence interval for the coefficients obtained from the ridge. However, doesn't the introduced bias on the estimators ruin the credibility of a confidence interval?

Also, I'm having trouble with finding a good way to summarize my new ridged model. Like the one you get from: summary(lm.fit) Which requires an lm object and not a lm.ridge object.