Package: mlAEWMA
Type: Package
Title: Machine Learning Based Adaptive EWMA Control Charts for Process
        Dispersion
Version: 0.1.0
Authors@R: c(
    person(given = "Gizem",
           family = "Atar",
           email = "gizem.atar@ostimteknik.edu.tr",
           role = c("aut", "cre")),
    person(given = "Ahmet",
           family = "Bursalı",
           email = "ahmet.bursali@huawei.com",
           role = "aut"),
    person(given = "Vilda",
           family = "Purutçuoğlu",
           email = "vpurutcu@metu.edu.tr",
           role = "aut")
  )
Description: Monitors the variance of a process with adaptive exponentially
    weighted moving average (AEWMA) control charts whose smoothing weight is
    predicted by a regression model fitted to the transformed subgroup
    variance. The subgroup variance is transformed as in Castagliola (2005)
    <doi:10.1002/qre.686>; the smoothing weight is predicted by one of nine
    pre-fitted models from four families (neural networks, support vector
    machines, regression trees and boosting, and a quantile regression with
    lagged inputs), following the parameter-free approach of Noor-ul-Amin et
    al. (2024) <doi:10.1038/s41598-024-82699-8>, related work on support
    vector regression based adaptive EWMA charts by Kazmi and Noor-ul-Amin
    (2024) <doi:10.1002/qre.3603>, and the adaptive EWMA idea of Capizzi and
    Masarotto (2003) <doi:10.1198/004017003000000023>. The comparison of
    the models is described in Atar, Purutçuoğlu and Bursalı (2026), accepted
    at the 3rd International Congress on Data Science and Information
    Technologies (INFTEC 2026).
    Control limits are calibrated by Monte Carlo simulation to a common
    in-control average run length of 370 for subgroups of size five.
    Functions are provided to run a chart on data, to compare all nine
    charts, and to estimate average run lengths by simulation.
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: e1071, gbm, kernlab, nnet, parallel, qrnn, rpart, stats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
LazyData: true
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-10-21 11:38:22 UTC; gizem
Author: Gizem Atar [aut, cre],
  Ahmet Bursalı [aut],
  Vilda Purutçuoğlu [aut]
Maintainer: Gizem Atar <gizem.atar@ostimteknik.edu.tr>
Repository: CRAN
Date/Publication: 2026-11-08 17:20:02 UTC
