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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0 |
rolling linux/jammy R-4.5 | cossonet_1.0.tar.gz |
98.0 KiB |
1.0 |
rolling linux/noble R-4.5 | cossonet_1.0.tar.gz |
97.8 KiB |
1.0 |
rolling source/ R- | cossonet_1.0.tar.gz |
20.2 KiB |
1.0 |
latest linux/jammy R-4.5 | cossonet_1.0.tar.gz |
98.0 KiB |
1.0 |
latest linux/noble R-4.5 | cossonet_1.0.tar.gz |
97.8 KiB |
1.0 |
latest source/ R- | cossonet_1.0.tar.gz |
20.2 KiB |
1.0 |
2026-04-26 source/ R- | cossonet_1.0.tar.gz |
20.2 KiB |
1.0 |
2026-04-23 source/ R- | cossonet_1.0.tar.gz |
20.2 KiB |
1.0 |
2026-04-09 windows/windows R-4.5 | cossonet_1.0.zip |
105.1 KiB |
1.0 |
2025-04-20 source/ R- | cossonet_1.0.tar.gz |
20.2 KiB |