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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:


Or try the development version on GitHub:



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

  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.