oscar

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.

Last 30 days

This package has been downloaded 169 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.

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Last 365 days

This package has been downloaded 2,473 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 36 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.