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: Linking:Please use the canonical form https://CRAN.R-project.org/package=oscar to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4