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BOSSreg
View on CRAN: Click
here
Download and install BOSSreg package within the R console
Install from CRAN:
install.packages("BOSSreg")
Install from Github:
library("remotes")
install_github("cran/BOSSreg") Install by package version:
library("remotes")
install_version("BOSSreg", "0.2.0") Attach the package and use:
library("BOSSreg")
Maintained by
Sen Tian
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-12-06
Latest Update: 2021-03-06
Description:
Best Orthogonalized Subset Selection (BOSS) is a least-squares (LS) based subset selection method, that performs best subset selection upon an orthogonalized basis of ordered predictors, with the computational effort of a single ordinary LS fit. This package provides a highly optimized implementation of BOSS and estimates a heuristic degrees of freedom for BOSS, which can be plugged into an information criterion (IC) such as AICc in order to select the subset from candidates. It provides various choices of IC, including AIC, BIC, AICc, Cp and GCV. It also implements the forward stepwise selection (FS) with no additional computational cost, where the subset of FS is selected via cross-validation (CV). CV is also an option for BOSS. For details see: Tian, Hurvich and Simonoff (2021), "On the Use of Information Criteria for Subset Selection in Least Squares Regression", .
How to cite:
Sen Tian (2019). BOSSreg: Best Orthogonalized Subset Selection (BOSS). R package version 0.2.0, https://cran.r-project.org/web/packages/BOSSreg. Accessed 27 Jun. 2026.
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Other R packages that BOSSreg depends,
imports, suggests or enhances
Complete documentation for BOSSreg
Functions, R codes and Examples using
the BOSSreg R package
Some associated functions: boss . calc.ic . coef.boss . coef.cv.boss . cv.boss . predict.boss . predict.cv.boss .
Some associated R codes: RcppExports.R . boss.R . cv.boss.R . ic.R . utils.R . Full BOSSreg package functions and examples
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