Package: EZtune
Type: Package
Title: Tunes AdaBoost, Elastic Net, Support Vector Machines, and
        Gradient Boosting Machines
Version: 3.1.2
Authors@R: person("Jill", "Lundell", email = "jflundell@gmail.com",
  role = c("aut", "cre"))
Maintainer: Jill Lundell <jflundell@gmail.com>
Description: Contains two functions that are intended to make tuning
        supervised learning methods easy. The eztune function uses a
        genetic algorithm or Hooke-Jeeves optimizer to find the best
        set of tuning parameters. The user can choose the optimizer,
        the learning method, and if optimization will be based on
        accuracy obtained through validation error, cross validation,
        or resubstitution. The function eztune_cv will compute a cross
        validated error rate. The purpose of eztune_cv is to provide a
        cross validated accuracy or MSE when resubstitution or
        validation data are used for optimization because error
        measures from both approaches can be misleading.
Depends: R (>= 3.1.0)
Imports: ada, e1071, GA, gbm, optimx, rpart, glmnet, ROCR, BiocStyle
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
Suggests: knitr, rmarkdown, mlbench, doParallel, parallel, dplyr,
        yardstick, rsample
VignetteBuilder: knitr
Config/pak/sysreqs: cmake make libuv1-dev
Repository: https://jillbo1000.r-universe.dev
Date/Publication: 2021-12-10 00:36:30 UTC
RemoteUrl: https://github.com/jillbo1000/eztune
RemoteRef: HEAD
RemoteSha: 6fe00c1d0ddb690786083d31f610a55bef88a2ce
NeedsCompilation: no
Packaged: 2026-07-02 06:53:44 UTC; root
Author: Jill Lundell [aut, cre]