Various R packages have been developed for the non-convex penalized estimation but they can only be applied to the smoothly clipped absolute deviation (SCAD) or minimax concave penalty (MCP). We develop an R package, entitled ncpen, for the non-convex penalized estimation in order to make data analysts to experience other non-convex penalties. The package ncpen implements a unified algorithm based on the convex concave procedure and modified local quadratic approximation algorithm, which can be applied to a broader range of non-convex penalties, including the SCAD and MCP as special examples. Many user-friendly functionalities such as generalized information criteria, cross-validation and ridge regularization are provided also.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-003.zip
lars, glmpath, glmnet, plus, sparsenet, cvplogit, ncvreg, ncpen, spls, Rcpp
MachineLearning, Survival, ChemPhys, HighPerformanceComputing, NumericalMathematics
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For attribution, please cite this work as
Kim, et al., "The R Journal: A Unified Algorithm for the Non-Convex Penalized Estimation: The ncpen Package", The R Journal, 2021
BibTeX citation
@article{RJ-2021-003,
author = {Kim, Dongshin and Lee, Sangin and Kwon, Sunghoon},
title = {The R Journal: A Unified Algorithm for the Non-Convex Penalized Estimation: The ncpen Package},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-003},
doi = {10.32614/RJ-2021-003},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {43-60}
}