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SPSP  

Selection by Partitioning the Solution Paths
View on CRAN: Click here


Download and install SPSP package within the R console
Install from CRAN:
install.packages("SPSP")

Install from Github:
library("remotes")
install_github("cran/SPSP")

Install by package version:
library("remotes")
install_version("SPSP", "0.2.0")



Attach the package and use:
library("SPSP")
Maintained by
Xiaorui (Jeremy) Zhu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-08-02
Latest Update: 2023-10-22
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.
How to cite:
Xiaorui (Jeremy) Zhu (2021). SPSP: Selection by Partitioning the Solution Paths. R package version 0.2.0, https://cran.r-project.org/web/packages/SPSP. Accessed 01 Sep. 2026.
Previous versions and publish date:
(2026-07-09 08:25), 0.1.0 (2021-08-02 10:40), 0.1.1 (2022-02-22 01:58)
Other packages that cited SPSP R package
View SPSP citation profile
Other R packages that SPSP depends, imports, suggests or enhances
Complete documentation for SPSP
Functions, R codes and Examples using the SPSP R package
Some associated functions: Fitting-Functions . HighDim . SPSP-package . SPSP . SPSP_step . 
Some associated R codes: RcppExports.R . SPSP.R . data.R . fitfun.SP.R .  Full SPSP package functions and examples
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