msaenet implements the multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions proposed in Xiao and Xu (2015) [PDF].
Nonconvex multi-step adaptive estimations based on MCP-net or SCAD-net are also supported.
Check vignette("msaenet") to get started.
You can install msaenet from CRAN:
install.packages("msaenet")
Or try the development version on GitHub:
remotes::install_github("nanxstats/msaenet")
To cite the msaenet package in publications, please use
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.
A BibTeX entry for LaTeX users is
@article{xiao2015multi,
title = {Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection},
author = {Nan Xiao and Qing-Song Xu},
journal = {Journal of Statistical Computation and Simulation},
volume = {85},
number = {18},
pages = {3755--3765},
year = {2015},
doi = {10.1080/00949655.2015.1016944}
} Adaptive Elastic-Net / Multi-Step Adaptive Elastic-Net
Adaptive MCP-Net / Multi-Step Adaptive MCP-Net
Adaptive SCAD-Net / Multi-Step Adaptive SCAD-Net
To contribute to this project, please take a look at the Contributing Guidelines first. Please note that the msaenet project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
msaenet is free and open source software, licensed under GPL-3.
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