oscar
Optimal Subset Cardinality Regression (OSCAR) Models Using the L0-Pseudonorm
Optimal Subset Cardinality Regression (OSCAR) models offer regularized linear regression using the L0-pseudonorm, conventionally known as the number of non-zero coefficients. The package estimates an optimal subset of features using the L0-penalization via cross-validation, bootstrapping and visual diagnostics. Effective Fortran implementations are offered along the package for finding optima for the DC-decomposition, which is used for transforming the discrete L0-regularized optimization problem into a continuous non-convex optimization task. These optimization modules include DBDC ('Double Bundle method for nonsmooth DC optimization' as described in Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited Memory Bundle Method for large-scale nonsmooth optimization' as in Haarala et al. (2004) <doi:10.1080/10556780410001689225>). The OSCAR models are comprehensively exemplified in Halkola et al. (2023) <doi:10.1371/journal.pcbi.1010333>). Multiple regression model families are supported: Cox, logistic, and Gaussian.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.2.1 |
rolling linux/jammy R-4.5 | oscar_1.2.1.tar.gz |
472.3 KiB |
1.2.1 |
rolling linux/noble R-4.5 | oscar_1.2.1.tar.gz |
484.0 KiB |
1.2.1 |
rolling source/ R- | oscar_1.2.1.tar.gz |
510.7 KiB |
1.2.1 |
latest linux/jammy R-4.5 | oscar_1.2.1.tar.gz |
472.3 KiB |
1.2.1 |
latest linux/noble R-4.5 | oscar_1.2.1.tar.gz |
484.0 KiB |
1.2.1 |
latest source/ R- | oscar_1.2.1.tar.gz |
510.7 KiB |
1.2.1 |
2026-04-26 source/ R- | oscar_1.2.1.tar.gz |
510.7 KiB |
1.2.1 |
2026-04-23 source/ R- | oscar_1.2.1.tar.gz |
510.7 KiB |
1.2.1 |
2026-04-09 windows/windows R-4.5 | oscar_1.2.1.zip |
647.3 KiB |
1.2.1 |
2025-04-20 source/ R- | oscar_1.2.1.tar.gz |
510.7 KiB |