abess: An R package for Best-Subset Selection in Polynomial Time

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Best-subset selection aims to find a small subset of predictors such that the resulting linear model is expected to have the most desirable prediction accuracy. This project implements a polynomial algorithm proposed by Zhu et al (2020) to solve the problem. It supports:


You can install the newest version of abess from github with:



A polynomial algorithm for best-subset selection problem. Junxian Zhu, Canhong Wen, Jin Zhu, Heping Zhang, Xueqin Wang. Proceedings of the National Academy of Sciences Dec 2020, 117 (52) 33117-33123; DOI: 10.1073/pnas.2014241117
Fan, J. and Lv, J. (2008), Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70: 849-911. https://doi.org/10.1111/j.1467-9868.2008.00674.x
Qiang Sun & Heping Zhang (2020) Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression, Journal of the American Statistical Association, DOI: 10.1080/01621459.2020.1737079