CatReg
Solution Paths for Linear and Logistic Regression Models with Categorical Predictors, with SCOPE Penalty
Computes solutions for linear and logistic regression models with potentially high-dimensional categorical predictors. This is done by applying a nonconvex penalty (SCOPE) and computing solutions in an efficient path-wise fashion. The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0.4 |
rolling linux/jammy R-4.5 | CatReg_2.0.4.tar.gz |
152.9 KiB |
2.0.4 |
rolling linux/noble R-4.5 | CatReg_2.0.4.tar.gz |
153.9 KiB |
2.0.4 |
rolling source/ R- | CatReg_2.0.4.tar.gz |
41.7 KiB |
2.0.4 |
latest linux/jammy R-4.5 | CatReg_2.0.4.tar.gz |
152.9 KiB |
2.0.4 |
latest linux/noble R-4.5 | CatReg_2.0.4.tar.gz |
153.9 KiB |
2.0.4 |
latest source/ R- | CatReg_2.0.4.tar.gz |
41.7 KiB |
2.0.4 |
2026-04-26 source/ R- | CatReg_2.0.4.tar.gz |
41.7 KiB |
2.0.4 |
2026-04-23 source/ R- | CatReg_2.0.4.tar.gz |
41.7 KiB |
2.0.4 |
2026-04-09 windows/windows R-4.5 | CatReg_2.0.4.zip |
470.7 KiB |
2.0.3 |
2025-04-20 source/ R- | CatReg_2.0.3.tar.gz |
40.5 KiB |