Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring.
| Version: | 1.0.6 |
| Depends: | R (≥ 2.10), glmnet |
| Suggests: | tools |
| Published: | 2020-07-03 |
| DOI: | 10.32614/CRAN.package.glmnetcr |
| Author: | Kellie J. Archer |
| Maintainer: | Kellie J. Archer <archer.43 at osu.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Citation: | glmnetcr citation info |
| Materials: | NEWS |
| CRAN checks: | glmnetcr results |
| Reference manual: | glmnetcr.pdf |
| Vignettes: |
glmnetcr: An R Package for Ordinal Response Prediction in High-Dimensional Data Settings |
| Package source: | glmnetcr_1.0.6.tar.gz |
| Windows binaries: | r-devel: glmnetcr_1.0.6.zip, r-release: glmnetcr_1.0.6.zip, r-oldrel: glmnetcr_1.0.6.zip |
| macOS binaries: | r-release (arm64): glmnetcr_1.0.6.tgz, r-oldrel (arm64): glmnetcr_1.0.6.tgz, r-release (x86_64): glmnetcr_1.0.6.tgz, r-oldrel (x86_64): glmnetcr_1.0.6.tgz |
| Old sources: | glmnetcr archive |
| Reverse imports: | BlockMissingData |
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