DESCRIPTION

Package: featR
Title: A Unified Toolkit for Feature Selection
Version: 0.1.0
Authors@R:
    person("Justin", "Chase", email = "jchase.msu@gmail.com",
           role = c("aut", "cre", "cph"))
Description: Filter, wrapper, and embedded feature-selection methods behind a
    consistent set of functions that share one calling convention and one
    return type: correlation and chi-squared filters, information gain, LASSO
    and elastic net, Bayesian model comparison, Boruta, recursive feature
    elimination, random forest importance, multivariate adaptive regression
    splines, support vector machine recursive feature elimination, stepwise
    selection, and principal component / singular value decomposition
    helpers. The implemented methods follow
    Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
    Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>,
    Friedman (1991) <doi:10.1214/aos/1176347963>,
    Breiman (2001) <doi:10.1023/A:1010933404324>,
    Guyon, Weston, Barnhill and Vapnik (2002) <doi:10.1023/A:1012487302797>,
    Kursa and Rudnicki (2010) <doi:10.18637/jss.v036.i11>, and
    Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>.
    Heavy modeling engines are optional and only required by the functions
    that use them.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
Roxygen: list(markdown = TRUE)
Config/roxygen2/version: 8.1.0
Depends:
    R (>= 4.1.0)
Imports:
    data.table,
    parallel,
    stats,
    utils,
    withr
Suggests:
    bigstatsr,
    Boruta,
    brms,
    caret,
    doParallel,
    e1071,
    earth,
    foreach,
    furrr,
    future,
    ggplot2,
    glmnet,
    kernlab,
    knitr,
    loo,
    MASS,
    Matrix,
    MLmetrics,
    pbapply,
    polycor,
    pROC,
    PRROC,
    randomForest,
    rmarkdown,
    RSpectra,
    testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
URL: https://github.com/elkronos/featR
BugReports: https://github.com/elkronos/featR/issues