Best subset glm using AIC, BIC, EBIC, BICq or
Cross-Validation. For the normal case, the 'leaps' is used.
Otherwise, a slower exhaustive search. The 'xtable' package is
needed for vignette 'SimExperimentBICq.Rnw' accompanying this
package.

R (>= 2.0.0), leaps, lars, ElemStatLearn
Best subset glm using AIC, BIC, EBIC, BICq or Cross-Validation. For the normal case, the 'leaps' is used. Otherwise, a slower exhaustive search. The 'xtable' package is needed for vignette 'SimExperimentBICq.Rnw' accompanying this package.

Best subset glm using information criteria or cross-validation.

Best Subset GLM and Regression Utilities
Best subset glm using information criteria or cross-validation. Implements PCR and PLS using AIC/BIC. Implements one-standard deviation rule for use with the `caret` package.

A.I. McLeod, Changjiang Xu and Yuanhao Lai
Best subset glm using information criteria or cross-validation,
carried by using 'leaps' algorithm (Furnival and Wilson, 1974) <doi:10.2307/1267601>
or complete enumeration (Morgan and Tatar, 1972) <doi:10.1080/00401706.1972.10488918>.
Implements PCR and PLS using AIC/BIC.
Implements one-standard deviation rule for use with the 'caret' package.

Generating Bootstrap Estimation Distributions of HR Data
c(person("Dan", "MacLean", role = c("aut","cre"), email = "dan.maclean@tsl.ac.uk") )
Creates plots showing scored HR experiments and plots of distribution of
means of ranks of HR score from bootstrapping. Authors (2019) <doi:10.5281/zenodo.3374507>.
dplyr, ggplot2, ggridges, magrittr, patchwork, rlang, stringr,
tibble

Best Subset Ridge Regression in Linear, Logistic, Poisson and
CoxPH Models
c(
person(given = "Canhong", family = "Wen", email = "wencanhong@gmail.com", role = c("aut")),
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 = "huly5@mail2.sysu.edu.cn", role = c("aut", "cre")),
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")))
Canhong Wen [aut],
Aijun Zhang [aut],
Shijie Quan [aut],
Liyuan Hu [aut, cre],
Kangkang Jiang [aut],
Yanhang Zhang [aut],
Jin Zhu [aut],
Xueqin Wang [aut]
An implementation of best subset ridge regression 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.