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subsamp  

Subsample Winner Algorithm for Variable Selection in Linear Regression with a Large Number of Variables
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("subsamp", "0.1.0")



Attach the package and use:
library("subsamp")
Maintained by
Yiying Fan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-11-14
Latest Update:
Description:
This subsample winner algorithm (SWA) for regression with a large-p data (X, Y) selects the important variables (or features) among the p features X in explaining the response Y. The SWA first uses a base procedure, here a linear regression, on each of subsamples randomly drawn from the p variables, and then computes the scores of all features, i.e., the p variables, according to the performance of these features collected in each of the subsample analyses. It then obtains the 'semifinalist' of the features based on the resulting scores and determines the 'finalists', i.e., the important features, from the 'semifinalist'. Fan, Sun and Qiao (2017) .
How to cite:
Yiying Fan (2017). subsamp: Subsample Winner Algorithm for Variable Selection in Linear Regression with a Large Number of Variables. R package version 0.1.0, https://cran.r-project.org/web/packages/subsamp. Accessed 04 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:10), 0.1.0 (2017-11-14 19:52)
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Functions, R codes and Examples using the subsamp R package
Some associated functions: subsamp . 
Some associated R codes: subsamp.R .  Full subsamp package functions and examples
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Maintainer: Yi Liu (view profile)

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