sparseR: Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials

An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods.

Version: 0.2.1
Depends: R (≥ 3.5)
Imports: ncvreg, rlang, magrittr, dplyr, recipes (≥ 1.0.0)
Suggests: survival, knitr, rmarkdown, kableExtra, testthat, covr, modeldata, MASS
Published: 2022-11-10
Author: Ryan Andrew Peterson ORCID iD [aut, cre]
Maintainer: Ryan Andrew Peterson <ryan.a.peterson at>
License: GPL-3
NeedsCompilation: no
Citation: sparseR citation info
Materials: README NEWS
CRAN checks: sparseR results


Reference manual: sparseR.pdf
Vignettes: sparseR


Package source: sparseR_0.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sparseR_0.2.1.tgz, r-oldrel (arm64): sparseR_0.2.1.tgz, r-release (x86_64): sparseR_0.2.1.tgz, r-oldrel (x86_64): sparseR_0.2.1.tgz
Old sources: sparseR archive


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