sparsestep
SparseStep Regression
Implements the SparseStep model for solving regression problems with a sparsity constraint on the parameters. The SparseStep regression model was proposed in Van den Burg, Groenen, and Alfons (2017) <arXiv:1701.06967>. In the model, a regularization term is added to the regression problem which approximates the counting norm of the parameters. By iteratively improving the approximation a sparse solution to the regression problem can be obtained. In this package both the standard SparseStep algorithm is implemented as well as a path algorithm which uses golden section search to determine solutions with different values for the regularization parameter.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.1 |
rolling linux/jammy R-4.5 | sparsestep_1.0.1.tar.gz |
47.5 KiB |
1.0.1 |
rolling linux/noble R-4.5 | sparsestep_1.0.1.tar.gz |
47.6 KiB |
1.0.1 |
rolling source/ R- | sparsestep_1.0.1.tar.gz |
8.9 KiB |
1.0.1 |
latest linux/jammy R-4.5 | sparsestep_1.0.1.tar.gz |
47.5 KiB |
1.0.1 |
latest linux/noble R-4.5 | sparsestep_1.0.1.tar.gz |
47.6 KiB |
1.0.1 |
latest source/ R- | sparsestep_1.0.1.tar.gz |
8.9 KiB |
1.0.1 |
2026-04-26 source/ R- | sparsestep_1.0.1.tar.gz |
8.9 KiB |
1.0.1 |
2026-04-23 source/ R- | sparsestep_1.0.1.tar.gz |
8.9 KiB |
1.0.1 |
2026-04-09 windows/windows R-4.5 | sparsestep_1.0.1.zip |
50.5 KiB |
1.0.1 |
2025-04-20 source/ R- | sparsestep_1.0.1.tar.gz |
8.9 KiB |