Packaged: 2018-05-27 19:27:22 UTC; IanMcLeod

Title: Generating Bootstrap Estimation Distributions of HR Data
Authors@R: c(person("Dan", "MacLean", role = c("aut","cre"), email = "dan.maclean@tsl.ac.uk") )
Description: 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>.
Imports: dplyr, ggplot2, ggridges, magrittr, patchwork, rlang, stringr,

Title: Best Subset Ridge Regression in Linear, Logistic, Poisson and
person(given = "Liyuan", family = "Hu", email = "huly5@mail2.sysu.edu.cn", role = c("aut", "cre")),
Description: 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.

Title: A Comprehensive R Package for Best Subset Selection
person(given = "Liyuan", family = "Hu", email = "huly5@mail2.sysu.edu.cn", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2064-8990")),
person(given = "Jin", family = "Zhu", email = "zhuj37@mail2.sysu.edu.cn", role = c("aut"), comment = c(ORCID = "0000-0001-8550-5822")),
person(given = "Canhong", family = "Wen", email = "wencanhong@gmail.com", role = c("aut")))
Description: The bestridge package is designed to provide a one-stand service for users to successfully carry out best ridge regression in various complex situations 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>. This package allows users to perform the regression, classification, count regression and censored regression for (ultra) high dimensional data, and it also supports advanced usages like group variable selection and nuisance variable selection.
Author: Liyuan Hu [aut, cre] (<https://orcid.org/0000-0003-2064-8990>),