{ "_id": "6af0e0ce94cdbf48db2b0e3c", "Package": "mlAEWMA", "Type": "Package", "Title": "Machine Learning Based Adaptive EWMA Control Charts for Process\nDispersion", "Version": "0.1.0", "Authors@R": "c(\nperson(given = \"Gizem\",\nfamily = \"Atar\",\nemail = \"gizem.atar@ostimteknik.edu.tr\",\nrole = c(\"aut\", \"cre\")),\nperson(given = \"Ahmet\",\nfamily = \"Bursalı\",\nemail = \"ahmet.bursali@huawei.com\",\nrole = \"aut\"),\nperson(given = \"Vilda\",\nfamily = \"Purutçuoğlu\",\nemail = \"vpurutcu@metu.edu.tr\",\nrole = \"aut\")\n)", "Description": "Monitors the variance of a process with adaptive\nexponentially weighted moving average (AEWMA) control charts\nwhose smoothing weight is predicted by a regression model\nfitted to the transformed subgroup variance. The subgroup\nvariance is transformed as in Castagliola (2005)\n; the smoothing weight is predicted by one\nof nine pre-fitted models from four families (neural networks,\nsupport vector machines, regression trees and boosting, and a\nquantile regression with lagged inputs), following the\nparameter-free approach of Noor-ul-Amin et al. (2024)\n, related work on support\nvector regression based adaptive EWMA charts by Kazmi and\nNoor-ul-Amin (2024) , and the adaptive\nEWMA idea of Capizzi and Masarotto (2003)\n. The comparison of the models\nis described in Atar, Purutçuoğlu and Bursalı (2026), accepted\nat the 3rd International Congress on Data Science and\nInformation Technologies (INFTEC 2026). Control limits are\ncalibrated by Monte Carlo simulation to a common in-control\naverage run length of 370 for subgroups of size five. Functions\nare provided to run a chart on data, to compare all nine\ncharts, and to estimate average run lengths by simulation.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "LazyData": "true", "Config/roxygen2/version": "8.1.0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-11-08 20:48:21 UTC", "User": "root" }, "Author": "Gizem Atar [aut, cre], Ahmet Bursalı [aut], Vilda Purutçuoğlu\n[aut]", "Maintainer": "Gizem Atar ", "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", "_user": "cran", "_type": "src", "_file": "mlAEWMA_0.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/5eba9bb7af8b0c26266627019d5cceb7de670b0bc5b75d579a377adb2081c5f6", "_filesize": 381322, "_sha256": "5eba9bb7af8b0c26266627019d5cceb7de670b0bc5b75d579a377adb2081c5f6", "_expires": 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