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: 2026-11-08 20:48:21 UTC; root
Author: Gizem Atar [aut, cre], Ahmet Bursalı [aut], Vilda Purutçuoğlu
        [aut]
Maintainer: Gizem Atar <gizem.atar@ostimteknik.edu.tr>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-11-08 19:51:53 UTC
RemoteUrl: https://github.com/cran/mlAEWMA
RemoteRef: HEAD
RemoteSha: d3f6dc9ccaa35276058f81b128fd09757db5281c