Regularization paths for SCAD- and MCP-penalized regression models
Efficient algorithms for fitting regularization paths for a variety of regression models (linear, logistic, Poisson, survival) penalized by MCP or SCAD, with optional additional L2 penalty.
Accepts a design matrix X and vector of responses y,
produces the regularization path over a grid of values for the tuning
parameter lambda. Also provides methods for plotting,
cross-validation-based inference, and for determining locally convex
regions of the coefficients paths.
See the "Getting started" vignette for a brief overview of how the package works.
Patrick Breheny <patrick-breheny@uiowa.edu>
Breheny P and Huang J. (2011) Coordinate descentalgorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. doi: 10.1214/10-AOAS388
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vignette("getting-started", package="ncvreg")
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