Estimation of sparse nonlinear functions in nonparametric regression using component selection and smoothing. Designed for the analysis of high-dimensional data, the models support various data types, including exponential family models and Cox proportional hazards models. The methodology is based on Lin and Zhang (2006) <doi:10.1214/009053606000000722>.
| Version: | 1.0 |
| Imports: | cosso, survival, stats, MASS, glmnet, graphics |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0), usethis (≥ 2.1.5), devtools |
| Published: | 2025-03-13 |
| DOI: | 10.32614/CRAN.package.cossonet |
| Author: | Jieun Shin [aut, cre] |
| Maintainer: | Jieun Shin <jieunstat at uos.ac.kr> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | cossonet results |
| Reference manual: | cossonet.html , cossonet.pdf |
| Vignettes: |
Estimation of sparse nonlinear functions in nonparametric regression using component selection and smoothing. (source, R code) |
| Package source: | cossonet_1.0.tar.gz |
| Windows binaries: | r-devel: cossonet_1.0.zip, r-release: cossonet_1.0.zip, r-oldrel: cossonet_1.0.zip |
| macOS binaries: | r-release (arm64): cossonet_1.0.tgz, r-oldrel (arm64): cossonet_1.0.tgz, r-release (x86_64): cossonet_1.0.tgz, r-oldrel (x86_64): cossonet_1.0.tgz |
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