Multi-Step Adaptive Estimation Methods for Sparse Regressions.

msaenet

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.

Installation

You can install msaenet from CRAN:

install.packages("msaenet")

Or try the development version on GitHub:

remotes::install_github("nanxstats/msaenet")

Citation

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{,
  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}
}

Gallery

Adaptive Elastic-Net / Multi-Step Adaptive Elastic-Net

Adaptive MCP-Net / Multi-Step Adaptive MCP-Net

Adaptive SCAD-Net / Multi-Step Adaptive SCAD-Net

Contribute

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.

License

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