DESCRIPTION

Package: oscar
Type: Package
Title: Optimal Subset Cardinality Regression (OSCAR) Models Using the L0-Pseudonorm
Version: 1.2.1
Date: 2023-10-02
Authors@R: c(
    person(given=c("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>). 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.
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,
    pROC
Suggests:
    ePCR,
    glmnet,
    knitr,
    rmarkdown
VignetteBuilder:
    knitr
Encoding: UTF-8
RoxygenNote: 7.2.3