Package: sparseR Type: Package Title: Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials Version: 0.3.2 Authors@R: person("Ryan Andrew", "Peterson", email = "ryan.a.peterson@cuanschutz.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4650-5798")) Description: 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) , 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. Suggests: survival, knitr, rmarkdown, kableExtra, testthat, covr, modeldata, MASS Imports: ncvreg, rlang, magrittr, dplyr, recipes (>= 1.0.0) Depends: R (>= 3.5) License: GPL-3 Encoding: UTF-8 LazyData: true VignetteBuilder: knitr RoxygenNote: 7.3.2 URL: https://petersonr.github.io/sparseR/, https://github.com/petersonR/sparseR/ Date: 2025-04-14 Config/pak/sysreqs: libicu-dev Repository: https://petersonr.r-universe.dev Date/Publication: 2025-04-14 17:57:15 UTC RemoteUrl: https://github.com/petersonr/sparser RemoteRef: HEAD RemoteSha: 225504235524f695586e4b26524dd04f2d2eec7b NeedsCompilation: no Packaged: 2026-06-06 09:38:16 UTC; root Author: Ryan Andrew Peterson [aut, cre] (ORCID: ) Maintainer: Ryan Andrew Peterson