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  "Type": "Package",
  "Title": "Variable Selection under Ranked Sparsity Principles for\nInteractions and Polynomials",
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  "Authors@R": "person(\"Ryan Andrew\", \"Peterson\", \nemail = \"ryan.a.peterson@cuanschutz.edu\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-4650-5798\"))",
  "Description": "An implementation of ranked sparsity methods, including\npenalized regression methods such as the sparsity-ranked lasso,\nits non-convex alternatives, and elastic net, as well as the\nsparsity-ranked Bayesian Information Criterion. As described in\nPeterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>,\nranked sparsity is a philosophy with methods primarily useful\nfor variable selection in the presence of prior informational\nasymmetry, which occurs in the context of trying to perform\nvariable selection in the presence of interactions and/or\npolynomials. Ultimately, this package attempts to facilitate\ndealing with cumbersome interactions and polynomials while not\navoiding them entirely. Typically, models selected under ranked\nsparsity principles will also be more transparent, having fewer\nfalsely selected interactions and polynomials than other\nmethods.",
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  "Date": "2025-04-14",
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  "Repository": "https://petersonr.r-universe.dev",
  "Date/Publication": "2025-04-14 17:57:15 UTC",
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  "Maintainer": "Ryan Andrew Peterson <ryan.a.peterson@cuanschutz.edu>",
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