cossonet: Sparse Nonparametric Regression for High-Dimensional Data

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

Documentation:

Reference manual:	cossonet.html , cossonet.pdf
Vignettes:	Estimation of sparse nonlinear functions in nonparametric regression using component selection and smoothing. (source, R code)

Downloads:

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

Linking:

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