Optimal Subset Cardinality Regression (OSCAR) Models Using the L0-Pseudonorm R package oscar version 1.2.1 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. Software Depends: R (>= 3.6.0) Imports: graphics, grDevices, hamlet, Matrix, methods, stats, survival, utils, pROC Suggests: ePCR, glmnet, knitr, rmarkdown Teemu Daniel Laajala <teelaa@utu.fi> Comprehensive R Archive Network (CRAN) Teemu Daniel Laajala [aut, cre] (ORCID: <https://orcid.org/0000-0002-7016-7354>), Kaisa Joki [aut], Anni Halkola [aut] GPL-3 2023-10-02 application/tgz https://CRAN.R-project.org/package=oscar doi:10.32614/CRAN.package.oscar