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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.
| 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 |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.