Regularization Paths for SCAD and MCP Penalized Regression Models

Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided. For more information, see Breheny and Huang (2011) or visit the ncvreg homepage < https://pbreheny.github.io/ncvreg/>.

Regularization paths for MCP and SCAD penalized regression models

ncvreg is an R package for fitting regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L₂ penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided.

• To get started using ncvreg, see the "getting started" vignette
• To learn more, follow the links under "Learn more" at the ncvreg website
• For details on the algorithms used by ncvreg, see the original article: Breheny P and Huang J (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232–253
• For more about the marginal false discovery rate idea used for post-selection inference, see Breheny P (2019) Marginal false discovery rates for penalized regression models. Biostatistics, 20: 299-314 and Miller R and Breheny P (2023) Feature-specific inference for penalized regression using local false discovery rates. Statistics in Medicine, 42: 1412–1429.
• I also teach a course on high-dimensional data analysis; the lecture notes are publicly available and may be helpful, in particular the lectures on MCP/SCAD and marginal FDR.

Installation

To install the latest release version from CRAN:

install.packages("ncvreg")

To install the latest development version from GitHub:

remotes::install_github("pbreheny/ncvreg")