sparsenet
Fit Sparse Linear Regression Models via Nonconvex Optimization
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.7 |
rolling source/ R- | sparsenet_1.7.tar.gz |
21.4 KiB |
1.7 |
rolling linux/jammy R-4.5 | sparsenet_1.7.tar.gz |
91.1 KiB |
1.7 |
rolling linux/noble R-4.5 | sparsenet_1.7.tar.gz |
91.0 KiB |
1.7 |
latest source/ R- | sparsenet_1.7.tar.gz |
21.4 KiB |
1.7 |
latest linux/jammy R-4.5 | sparsenet_1.7.tar.gz |
91.1 KiB |
1.7 |
latest linux/noble R-4.5 | sparsenet_1.7.tar.gz |
91.0 KiB |
1.7 |
2026-04-26 source/ R- | sparsenet_1.7.tar.gz |
21.4 KiB |
1.7 |
2026-04-23 source/ R- | sparsenet_1.7.tar.gz |
21.4 KiB |
1.7 |
2026-04-09 windows/windows R-4.5 | sparsenet_1.7.zip |
267.4 KiB |
1.7 |
2025-04-20 source/ R- | sparsenet_1.7.tar.gz |
21.4 KiB |