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Functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) <doi:10.48550/arXiv.1712.03561>. The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit.
Version: | 1.0.2 |
Imports: | Rcpp (≥ 0.12.12) |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | testthat, glmnet, MASS |
Published: | 2020-02-05 |
DOI: | 10.32614/CRAN.package.SplitReg |
Author: | Anthony Christidis, Ezequiel Smucler, Ruben Zamar |
Maintainer: | Anthony Christidis <anthony.christidis at stat.ubc.ca> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
Materials: | README NEWS |
CRAN checks: | SplitReg results |
Reference manual: | SplitReg.pdf |
Package source: | SplitReg_1.0.2.tar.gz |
Windows binaries: | r-devel: SplitReg_1.0.2.zip, r-release: SplitReg_1.0.2.zip, r-oldrel: SplitReg_1.0.2.zip |
macOS binaries: | r-release (arm64): SplitReg_1.0.2.tgz, r-oldrel (arm64): SplitReg_1.0.2.tgz, r-release (x86_64): SplitReg_1.0.2.tgz, r-oldrel (x86_64): SplitReg_1.0.2.tgz |
Old sources: | SplitReg archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.