Package: sparseR 0.3.0

Ryan Andrew Peterson

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.

Authors:Ryan Andrew Peterson [aut, cre]

sparseR_0.3.0.tar.gz
sparseR_0.3.0.tar.gz(r-4.5-noble)sparseR_0.3.0.tar.gz(r-4.4-noble)
sparseR_0.3.0.tgz(r-4.4-emscripten)sparseR_0.3.0.tgz(r-4.3-emscripten)
sparseR.pdf |sparseR.html✨
sparseR/json (API)
NEWS

Peer review:

Bug tracker:https://github.com/petersonr/sparser/issues

12 exports 0.23 score 56 dependencies 174 downloads

Last updated 9 days agofrom:3bb814a4db

Exports:%>%EBICeffect_plotget_penaltiesRAICRBICsparseRsparseR_prepsparseRBIC_bootstrapsparseRBIC_sampsplitsparseRBIC_stepstep_center_to

Dependencies:classcliclockcodetoolscpp11data.tablediagramdigestdplyrellipsisfansifuturefuture.applygenericsglobalsgluegowerhardhatipredKernSmoothlatticelavalifecyclelistenvlubridatemagrittrMASSMatrixncvregnnetnumDerivparallellypillarpkgconfigprodlimprogressrpurrrR6RcpprecipesrlangrpartshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrswithr