The R Journal: article published in 2017, volume 9:1

spcadjust: An R Package for Adjusting for Estimation Error in Control Charts PDF download
Axel Gandy and Jan Terje Kvaløy , The R Journal (2017) 9:1, pages 458-476.

Abstract In practical applications of control charts the in-control state and the corresponding chart parameters are usually estimated based on some past in-control data. The estimation error then needs to be accounted for. In this paper we present an R package, spcadjust, which implements a bootstrap based method for adjusting monitoring schemes to take into account the estimation error. By bootstrapping the past data this method guarantees, with a certain probability, a conditional performance of the chart. In spcadjust the method is implement for various types of Shewhart, CUSUM and EWMA charts, various performance criteria, and both parametric and non-parametric bootstrap schemes. In addition to the basic charts, charts based on linear and logistic regression models for risk adjusted monitoring are included, and it is easy for the user to add further charts. Use of the package is demonstrated by examples.

Received: 2016-11-16; online 2017-05-10
CRAN packages: spcadjust, surveillance, spc, qcc, IQCC, qcr, edcc, MSQC
CRAN Task Views implied by cited CRAN packages: Environmetrics, SpatioTemporal, TimeSeries

CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {Axel Gandy and Jan Terje Kvaløy},
  title = {{spcadjust: An R Package for Adjusting for Estimation Error
          in Control Charts}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-014},
  url = {},
  pages = {458--476},
  volume = {9},
  number = {1}