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sparseR
Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials
View on CRAN: Click
here
Download and install sparseR package within the R console
Install from CRAN:
install.packages("sparseR")
Install from Github:
library("remotes")
install_github("cran/sparseR") Install by package version:
library("remotes")
install_version("sparseR", "0.3.1") Attach the package and use:
library("sparseR")
Maintained by
Ryan Andrew Peterson
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.sparseR . https://petersonr.github.io/sparseR/ . https://github.com/petersonR/sparseR/ . sparseR citation info . sparseR results . sparseR.pdf . Using the 'sparseR' package . sparseR_0.3.1.tar.gz . sparseR_0.3.1.zip . sparseR_0.3.1.zip . sparseR_0.3.1.zip . sparseR_0.3.1.tgz . sparseR_0.3.1.tgz . sparseR_0.3.1.tgz . sparseR_0.3.1.tgz . sparseR_0.3.1.tgz . sparseR_0.3.1.tgz . sparseR archive . https://CRAN.R-project.org/package=sparseR .
First Published: 2022-08-17
Latest Update: 2023-12-06
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) <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.
How to cite:
Ryan Andrew Peterson (2022). sparseR: Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials. R package version 0.3.1, https://cran.r-project.org/web/packages/sparseR. Accessed 29 Mar. 2025.
Previous versions and publish date:
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Complete documentation for sparseR
Functions, R codes and Examples using
the sparseR R package
Some associated functions: custom_ics . datasets . effect_plot . get_penalties . pipe . plot.sparseR . predict.sparseR . print.sparseR . sparseR-package . sparseR . sparseRBIC_bootstrap . sparseRBIC_sampsplit . sparseRBIC_step . sparseR_prep . step_center_to . summary.sparseR .
Some associated R codes: custom_ICs.R . custom_step_helpers.R . data.R . get_penalties.R . plot.sparseR.R . sparseR-package.R . sparseR.R . sparseRBIC_bootstrap.R . sparseRBIC_sampsplit.R . sparseRBIC_step.R . sparseR_prep.R . step_center_to.R . utils-pipe.R . Full sparseR package functions and examples
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