Package: abess
Type: Package
Title: Fast Best Subset Selection
Version: 0.4.8
Date: 2023-09-19
Authors@R: 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 = "Zezhi", family = "Wang", email = "homura@mail.ustc.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.cn", role = c("aut")),
  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 = "Borui", family = "Tang", email = "tangborui@mail.ustc.edu.cn", role = c("aut")),
  person(given = "Shiyun", family = "Lin", email = "shiyunlin@stu.pku.edu.cn", role = c("aut")),
  person(given = "Junxian", family = "Zhu", email = "adaizjx@163.com", 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")
  )
Maintainer: Jin Zhu <zhuj37@mail2.sysu.edu.cn>
Description: Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package 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 for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.  
License: GPL (>= 3) | file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.1.0)
Imports: Rcpp, MASS, methods, Matrix
LinkingTo: Rcpp, RcppEigen
RoxygenNote: 7.2.3
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/abess-team/abess,
        https://abess-team.github.io/abess/,
        https://abess.readthedocs.io
BugReports: https://github.com/abess-team/abess/issues
NeedsCompilation: yes
Packaged: 2023-09-19 11:07:36 UTC; wang
Author: Jin Zhu [aut, cre] (<https://orcid.org/0000-0001-8550-5822>),
  Zezhi Wang [aut],
  Liyuan Hu [aut],
  Junhao Huang [aut],
  Kangkang Jiang [aut],
  Yanhang Zhang [aut],
  Borui Tang [aut],
  Shiyun Lin [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)
Repository: CRAN
Date/Publication: 2023-09-19 13:20:02 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 01:11:20 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
