BOSO: Bilevel Optimization Selector Operator

A novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). The main contribution is the use a bilevel optimization problem to select the variables in the training problem that minimize the error in the validation set. Preprint available: [Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X., Prosper, F., & Planes, F. J. (2020). "BOSO: a novel feature selection algorithm for linear regression with high-dimensional data." bioRxiv. <doi:10.1101/2020.11.18.388579>]. In order to run the vignette, it is recommended to install the 'bestsubset' package, using the following command: devtools::install_github(repo="ryantibs/best-subset", subdir="bestsubset"). If you do not have gurobi, run devtools::install_github(repo="lvalcarcel/best-subset", subdir="bestsubset").

Version: 1.0.3
Depends: R (≥ 4.0)
Imports: Matrix, MASS, methods
Suggests: cplexAPI, testthat, glmnet, knitr, rmarkdown, ggplot2, ggpubr, dplyr, kableExtra, devtools, BiocStyle, bestsubset
Published: 2021-07-01
Author: Luis V. Valcarcel ORCID iD [aut, cre, ctb], Edurne San Jose-Eneriz ORCID iD [aut], Xabier Cendoya ORCID iD [aut, ctb], Angel Rubio ORCID iD [aut, ctb], Xabier Agirre ORCID iD [aut], Felipe Prósper ORCID iD [aut], Francisco J. Planes ORCID iD [aut, ctb]
Maintainer: Luis V. Valcarcel <lvalcarcel at>
License: GPL-3
NeedsCompilation: no
SystemRequirements: IBM ILOG CPLEX (>= 12.1)
Materials: README NEWS
CRAN checks: BOSO results


Reference manual: BOSO.pdf
Vignettes: BOSO
Package source: BOSO_1.0.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BOSO_1.0.3.tgz, r-release (x86_64): BOSO_1.0.3.tgz, r-oldrel: BOSO_1.0.3.tgz


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