sparseR-package

The <code>sparseR</code> package implements various techniques for selecting from
a set of interaction and polynomial terms under ranked sparsity. Additional
tools for data pre-processing, post-selection inference, and visualization
are also included.

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.

Ryan Andrew Peterson

sparseR

Variable Selection under Ranked Sparsity Principles for
Interactions and Polynomials

sparseR-package function

Maintainer: Ryan Andrew Peterson <a href="/link/ryan.a.peterson%40cuanschutz.edu?package=sparseR&version=0.3.2" data-mini-rdoc="sparseR::ryan.a.peterson@cuanschutz.edu">ryan.a.peterson@cuanschutz.edu</a> (<a href="https://orcid.org/0000-0002-4650-5798">ORCID</a>)

Author

sparseR: Implement ranked sparsity for selecting interactions and polynomials — sparseR-package

Maintainer: Ryan Andrew Peterson <a href='mailto:ryan.a.peterson@cuanschutz.edu'>ryan.a.peterson@cuanschutz.edu</a> (<a href='https://orcid.org/0000-0002-4650-5798'>ORCID</a>)

sparseR-package: sparseR: Implement ranked sparsity for selecting interactions and polynomials

Description

Arguments

Author

See Also