| Name : R-oscar
| |
| Version : 1.2.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.12
| Date : 2024-06-14 11:38:19
|
| Group : Development/Libraries/Other
| Source RPM : R-oscar-1.2.1-lp153.1.12.src.rpm
|
| Size : 0.79 MB
| |
| Packager : (none)
| |
| Summary : Optimal Subset Cardinality Regression (OSCAR) Models Using the L0-Pseudonorm
|
Description :
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
