oscar: Optimal Subset Cardinality Regression (OSCAR) Models Using theL0-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.
| Version: | 1.2.1 |
| Depends: | R (≥ 3.6.0) |
| Imports: | graphics, grDevices,hamlet,Matrix, methods, stats,survival, utils,pROC |
| Suggests: | ePCR,glmnet,knitr,rmarkdown |
| Published: | 2023-10-02 |
| DOI: | 10.32614/CRAN.package.oscar |
| Author: | Teemu Daniel Laajala [aut, cre], Kaisa Joki [aut], Anni Halkola [aut] |
| Maintainer: | Teemu Daniel Laajala <teelaa at utu.fi> |
| BugReports: | https://github.com/Syksy/oscar/issues |
| License: | GPL-3 |
| URL: | https://github.com/Syksy/oscar |
| NeedsCompilation: | yes |
| Citation: | oscar citation info |
| Materials: | NEWS |
| CRAN checks: | oscar results |
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