CRAN/E | ncpen

ncpen

Unified Algorithm for Non-convex Penalized Estimation for Generalized Linear Models

Installation

About

An efficient unified nonconvex penalized estimation algorithm for Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit, and Cox proportional hazard 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 high-dimensional data (data set with many variables), the algorithm selects relevant variables producing a parsimonious regression model. Kim, D., Lee, S. and Kwon, S. (2018) , Lee, S., Kwon, S. and Kim, Y. (2016) doi:10.1016/j.csda.2015.08.019, Kwon, S., Lee, S. and Kim, Y. (2015) doi:10.1016/j.csda.2015.07.001. (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine Graziadio Business School and the National Research Foundation of Korea.)

github.com/zeemkr/ncpen
Bug report File report

Key Metrics

Version 1.0.0
R ≥ 3.4
Published 2018-11-17 2156 days ago
Needs compilation? yes
License GPL (≥ 3)
CRAN checks ncpen results
Language en-US

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Maintainer

Maintainer

Dongshin Kim

Authors

Dongshin Kim

aut / cre / cph

Sunghoon Kwon

aut / cph

Sangin Lee

aut / cph

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

ncpen archive

Depends

R ≥ 3.4

Imports

Rcpp ≥ 0.11.2

LinkingTo

Rcpp
RcppArmadillo