Adaptive Best Subset Selection in Polynomial Time
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person(given = "Jin", family = "Zhu", email = "zhuj37@mail2.sysu.edu.cn", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8550-5822")),
person(given = "Kangkang", family = "Jiang", email = "jiangkk3@mail2.sysu.edu.cn", role = c("aut")),
person(given = "Yanhang", family = "Zhang", email = "zhangyh98@ruc.edu.cn", role = c("aut")),
person(given = "Liyuan", family = "Hu", email = "huly5@mail2.sysu.edu.cn", role = c("aut")),
person(given = "Junxian", family = "Zhu", email = "zhujunx@mail2.sysu.edu.cn", role = c("aut")),
person(given = "Canhong", family = "Wen", email = "wencanhong@gmail.com", role = c("aut")),
person(given = "Heping", family = "Zhang", email = "heping.zhang@yale.edu", role = c("aut"), comment = c(ORCID = "0000-0002-0688-4076")),
person(given = "Xueqin", family = "Wang", email = "wangxq20@ustc.edu.cn", role = c("aut"), comment = c(ORCID = "0000-0001-5205-9950"))
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Extremely efficient toolkit for solving the best subset selection problem in linear regression, logistic regression, Poisson regression, Cox proportional hazard model, multiple-response Gaussian, and multinomial regression. It implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times.
Jin Zhu [aut, cre] (<https://orcid.org/0000-0001-8550-5822>),
Kangkang Jiang [aut],
Yanhang Zhang [aut],
Liyuan Hu [aut],
Junxian Zhu [aut],
Canhong Wen [aut],
Heping Zhang [aut] (<https://orcid.org/0000-0002-0688-4076>),
Xueqin Wang [aut] (<https://orcid.org/0000-0001-5205-9950>)

c(
person(given = "Jin", family = "Zhu", email = "zhuj37@mail2.sysu.edu.cn", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8550-5822")),
person(given = "Kangkang", family = "Jiang", email = "jiangkk3@mail2.sysu.edu.cn", role = c("aut")),
person(given = "Yanhang", family = "Zhang", email = "zhangyh98@ruc.edu.cn", role = c("aut")),
person(given = "Liyuan", family = "Hu", email = "huly5@mail2.sysu.edu.cn", role = c("aut")),
person(given = "Junhao", family = "Huang", email = "huangjh256@mail2.sysu.edu.can", role = c("aut")),
person(given = "Junxian", family = "Zhu", email = "zhujunx@mail2.sysu.edu.cn", role = c("aut")),
person(given = "Canhong", family = "Wen", email = "wencanhong@gmail.com", role = c("aut")),
person(given = "Heping", family = "Zhang", email = "heping.zhang@yale.edu", role = c("aut"), comment = c(ORCID = "0000-0002-0688-4076")),
person(given = "Xueqin", family = "Wang", email = "wangxq20@ustc.edu.cn", role = c("aut"), comment = c(ORCID = "0000-0001-5205-9950")),
person("spectra contributors", role = c("cph"), comment = "Spectra implementation")
)
Extremely efficient toolkit for solving the best subset selection problem in linear regression, logistic regression, Poisson regression, Cox proportional hazard model, multiple-response regression, multinomial logistic regression, and (sequential) principal component analysis. It implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times.
Jin Zhu [aut, cre] (<https://orcid.org/0000-0001-8550-5822>),
Kangkang Jiang [aut],
Yanhang Zhang [aut],
Liyuan Hu [aut],
Junhao Huang [aut],
Junxian Zhu [aut],
Canhong Wen [aut],
Heping Zhang [aut] (<https://orcid.org/0000-0002-0688-4076>),
Xueqin Wang [aut] (<https://orcid.org/0000-0001-5205-9950>),
spectra contributors [cph] (Spectra implementation)