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Computes solutions for linear and logistic regression models with potentially high-dimensional categorical predictors. This is done by applying a nonconvex penalty (SCOPE) and computing solutions in an efficient path-wise fashion. The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.
| Version: | 2.0.3 |
| Imports: | Rcpp (≥ 1.0.1), Rdpack |
| LinkingTo: | Rcpp |
| Published: | 2021-06-14 |
| Author: | Benjamin Stokell [aut], Daniel Grose [ctb, cre], Rajen Shah [ctb] |
| Maintainer: | Daniel Grose <dan.grose at lancaster.ac.uk> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | CatReg results |
| Reference manual: | CatReg.pdf |
| Package source: | CatReg_2.0.3.tar.gz |
| Windows binaries: | r-devel: CatReg_2.0.3.zip, r-release: CatReg_2.0.3.zip, r-oldrel: CatReg_2.0.3.zip |
| macOS binaries: | r-release (arm64): CatReg_2.0.3.tgz, r-oldrel (arm64): CatReg_2.0.3.tgz, r-release (x86_64): CatReg_2.0.3.tgz, r-oldrel (x86_64): CatReg_2.0.3.tgz |
| Old sources: | CatReg archive |
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