ncpen: Nonconvex Penalized Estimation for Generalized Linear Models

An efficient unified algorithm for estimating the nonconvex penalized linear, logistic and Poisson regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For a data set with many variables (high-dimensional data), the algorithm selects relevant variables producing a parsimonious regression model. Kwon, S., Lee, S. and Kim, Y. (2015) <doi:10.1016/j.csda.2015.07.001>, Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>. (This project is funded by Julian Virtue Professorship from Center for Applied Research at Graziadio School of Business and Management at Pepperdine University.)

Version: 0.2.0
Depends: R (≥ 3.4)
Imports: Rcpp (≥ 0.11.2)
LinkingTo: Rcpp, RcppArmadillo
Published: 2018-02-21
Author: Dongshin Kim [aut, cre, cph], Sunghoon Kwon [aut, cph], Sangin Lee [aut, cph]
Maintainer: Dongshin Kim < at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: ncpen results


Reference manual: ncpen.pdf
Package source: ncpen_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel: not available
OS X El Capitan binaries: r-release: ncpen_0.2.0.tgz
OS X Mavericks binaries: r-oldrel: not available


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