Package: BeSS
Type: Package
Title: Best Subset Selection /Ridge Regression in Linear, Logistic,
        Poisson and CoxPH Models
Version: 2.0.2
Date: 2021-01-23
Authors@R: c(
  person(given = "Canhong", family = "Wen", email = "wencanhong@gmail.com", role = c("aut", "cre")),
  person(given = "Aijun", family = "Zhang", email = "ajzhang@hku.hk", role = c("aut")),
  person(given = "Shijie", family = "Quan", email = "329773935@qq.com", role = c("aut")),
  person(given = "Liyuan", family = "Hu", email = "1249096170@qq.com", 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 = "Jin", family = "Zhu", email = "zhuj37@mail2.sysu.edu.cn", role = c("aut")),
  person(given = "Xueqin", family = "Wang", email = "wangxq20@ustc.edu.cn", role = c("aut")))
Author: Canhong Wen [aut, cre],
  Aijun Zhang [aut],
  Shijie Quan [aut],
  Liyuan Hu [aut],
  Kangkang Jiang [aut],
  Yanhang Zhang [aut],
  Jin Zhu [aut],
  Xueqin Wang [aut]
Maintainer: Canhong Wen <wencanhong@gmail.com>
Description: An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.
License: GPL-3
Depends: R (>= 3.5.0)
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 1.0.3), Matrix(>= 1.2-6), MASS, pheatmap, survival
LinkingTo: Rcpp, RcppEigen
RoxygenNote: 7.1.1
Suggests: knitr, HCmodelSets, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/Mamba413/bess
BugReports: https://github.com/Mamba413/bess/issues
NeedsCompilation: yes
Packaged: 2021-01-23 02:50:53 UTC; test
Repository: CRAN
Date/Publication: 2021-01-23 07:10:02 UTC
