Package: EZtune
Type: Package
Title: Tunes AdaBoost, Support Vector Machines, and Gradient Boosting
        Machines
Version: 1.0.0
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 quasi-Newton-Raphson 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 resubstitution or cross-validation. The function 
    eztune.cv will compute a cross-validated error rate. The purpose 
    of eztune.cv is to provide a cross-validated accuracy when resubstitution
    is used for optimization because resubstitution
    typically produces an accuracy that is too high.
Depends: R (>= 3.1.0)
Imports: ada, e1071, GA, gbm, mlbench, parallel, doParallel
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2018-10-05 07:03:07 UTC; jflun
Author: Jill Lundell [aut, cre]
Repository: CRAN
Date/Publication: 2018-10-14 16:50:17 UTC
