Package: oscar
Type: Package
Title: Optimal Subset Cardinality Regression (OSCAR) Models Using the
        L0-Pseudonorm
Version: 1.0.4
Date: 2022-05-21
Authors@R: c(
    person(given="Teemu Daniel", family="Laajala", role=c("aut", "cre"), email="teelaa@utu.fi", comment = c(ORCID = "0000-0002-7016-7354")),
    person(given="Kaisa", family="Joki", role=c("aut"), email="kjjoki@utu.fi"),
    person(given="Anni", family="Halkola", role=c("aut"), email="ansuha@utu.fi"))
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>).
    Multiple regression model families are supported: Cox, logistic,
    and Gaussian.
License: GPL-3
LazyData: true
URL: https://github.com/Syksy/oscar
BugReports: https://github.com/Syksy/oscar/issues
NeedsCompilation: yes
Depends: R (>= 3.6.0)
Imports: graphics, grDevices, hamlet, Matrix, methods, stats, survival,
        utils
Suggests: ePCR, glmnet, knitr, pROC, rmarkdown
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.2.0
Packaged: 2022-05-21 16:29:11 UTC; teemu
Author: Teemu Daniel Laajala [aut, cre]
    (<https://orcid.org/0000-0002-7016-7354>),
  Kaisa Joki [aut],
  Anni Halkola [aut]
Maintainer: Teemu Daniel Laajala <teelaa@utu.fi>
Repository: CRAN
Date/Publication: 2022-05-23 23:20:10 UTC
