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.

Paper Citation

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}
}

Installation

To download and install msaenet from CRAN:

install.packages("msaenet")

Or try the development version on GitHub:

# install.packages("devtools")
devtools::install_github("road2stat/msaenet")

Browse the vignette (can be opened with vignette("msaenet") in R) for a quick-start.

Visit the website for more documentation.

Contribute

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.

License

msaenet is free and open source software, licensed under GPL-3.