abess-team / abess-a-fast-best-subset-selection-library-in-python-and-r

Reproducible materials for "abess: A Fast Best-Subset Selection Library in Python and R"

Reproducible materials

This repository contains scripts to reproduce the numerical results analysis described in "abess: A Fast Best-Subset Selection Library in Python and R". A step-by-step instruction for reproducting is provided in this page.

Citations

Please cite the following publications if you make use of the material here.

• Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin and Junxian Zhu (2022). abess: A Fast Best-Subset Selection Library in Python and R. Journal of Machine Learning Research, 23(202), 1-7.

The corresponding BibteX entries:

@article{JMLR:v23:21-1060,
  author  = {Jin Zhu and Xueqin Wang and Liyuan Hu and Junhao Huang and Kangkang Jiang and Yanhang Zhang and Shiyun Lin and Junxian Zhu},
  title   = {abess: A Fast Best-Subset Selection Library in Python and R},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {202},
  pages   = {1--7},
  url     = {http://jmlr.org/papers/v23/21-1060.html}
}

Contact

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