Package: SPSP
Type: Package
Title: Selection by Partitioning the Solution Paths
Description: An implementation of the feature Selection procedure by
        Partitioning the entire Solution Paths (namely SPSP) to
        identify the relevant features rather than using a single
        tuning parameter. By utilizing the entire solution paths, this
        procedure can obtain better selection accuracy than the
        commonly used approach of selecting only one tuning parameter
        based on existing criteria, cross-validation (CV), generalized
        CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018)
        <doi:10.1214/18-EJS1434>). It is more stable and accurate (low
        false positive and false negative rates) than other variable
        selection approaches. In addition, it can be flexibly coupled
        with the solution paths of Lasso, adaptive Lasso, ridge
        regression, and other penalized estimators.
Version: 0.2.0.9000
Date: 2023-10-21
Authors@R: c(
  person(given = "Xiaorui (Jeremy)", family = "Zhu", email = "zhuxiaorui1989@gmail.com", role = c("aut", "cre")),
  person(given = "Yang", family = "Liu", email = "yliu23@fhcrc.org", role = c("aut")), 
  person(given = "Peng", family = "Wang", email = "wangp9@ucmail.uc.edu", role = c("aut"))
  )
Maintainer: Xiaorui (Jeremy) Zhu <zhuxiaorui1989@gmail.com>
URL: https://xiaorui.site/SPSP/, https://github.com/XiaoruiZhu/SPSP
BugReports: https://github.com/XiaoruiZhu/SPSP/issues
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.7), glmnet, ncvreg, Matrix, lars
LinkingTo: Rcpp
Suggests: testthat (>= 3.0.0), MASS
Config/testthat/edition: 3
RoxygenNote: 7.2.3
Repository: https://xiaoruizhu.r-universe.dev
Date/Publication: 2023-12-06 05:15:29 UTC
RemoteUrl: https://github.com/xiaoruizhu/spsp
RemoteRef: HEAD
RemoteSha: c4b4778bb36cb3f60e638c89f417f5fe40c0c609
NeedsCompilation: yes
Packaged: 2026-07-04 02:02:21 UTC; root
Author: Xiaorui (Jeremy) Zhu [aut, cre],
  Yang Liu [aut],
  Peng Wang [aut]