msaenet
implements the multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions proposed in Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944> (PDF).
Nonconvex multi-step adaptive estimations based on MCP-net or SCAD-net are also supported.
Formatted citation:
Nan Xiao and Qing-Song Xu. (2015). Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection. Journal of Statistical Computation and Simulation 85(18), 3755-3765.
BibTeX entry:
@article{xiao2015msaenet,
title={Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection},
author={Xiao, Nan and Xu, Qing-Song},
journal={Journal of Statistical Computation and Simulation},
volume={85},
number={18},
pages={3755--3765},
year={2015},
publisher={Taylor \& Francis}
}
To download and install msaenet
from CRAN:
install.packages("msaenet")
Or try the development version on GitHub:
# install.packages("devtools")
devtools::install_github("nanxstats/msaenet")
Browse the vignette (can be opened with vignette("msaenet")
in R) for a quick-start.
To contribute to this project, please take a look at the Contributing Guidelines first. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
msaenet is free and open source software, licensed under GPL-3.
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