r-pcLasso

Description

Principal Components Lasso.

Description

A method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression' <arXiv:1810.04651>.

README.md

cran.r-project.org

Bug fixes:

predict.pcLasso now works when family = “binomial” (previously, the intercept term was being added in an incorrect manner).
Previously, standardize = TRUE scaled the beta coefficients and intercept a0 incorrectly. This has been fixed.
pcLasso now generates lambda values for the objective function RSS/(2n) + penalty, instead of that for RSS/2 + penalty.

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