EATME: An R package for EWMA control charts with adjustments of measurement error

In this paper, we introduce an R package EATME, which is known as Exponentially weighted moving average (EWMA) control chart with Adjustments To Measurement Error. The main purpose of this package is to correct for measurement error effects in continuous or binary random variables and develop the corrected control charts based on the EWMA statistic. In addition, the corrected control charts can detect out-of-control process accurately. The package contains a function to generate synthetic data and includes functions to determine the reasonable coefficient of control limit as well as estimate average run length. Moreover, for the visualization, we also provide the control charts to show the monitoring of in-control and out-of-control process. Finally, the functions in this package are clearly demonstrated, and numerical studies show the validity of the package.

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Publication Details

Journal
PLoS ONE
Published
2024-10-03
DOI
https://doi.org/10.1371/journal.pone.0308828
Citations
4
Primary Topic
Advanced Statistical Process Monitoring
Type
article
Field-Weighted Citation Impact
1.85

Funders

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article

EATME: An R package for EWMA control charts with adjustments of measurement error

Li‐Pang Chen, Cheng‐Kuan Lin
4 citations
PLoS ONE
Advanced Statistical Process Monitoring
1.85
article

EATME: An R package for EWMA control charts with adjustments of measurement error

Li‐Pang Chen, Cheng‐Kuan Lin
article en
4 citations

Abstract

In this paper, we introduce an R package EATME, which is known as Exponentially weighted moving average (EWMA) control chart with Adjustments To Measurement Error. The main purpose of this package is to correct for measurement error effects in continuous or binary random variables and develop the corrected control charts based on the EWMA statistic. In addition, the corrected control charts can detect out-of-control process accurately. The package contains a function to generate synthetic data and includes functions to determine the reasonable coefficient of control limit as well as estimate average run length. Moreover, for the visualization, we also provide the control charts to show the monitoring of in-control and out-of-control process. Finally, the functions in this package are clearly demonstrated, and numerical studies show the validity of the package.

PLoS ONEVol. 19(10)
National Chengchi University (TW)
National Science and Technology Council
Openalex Percentile: Top 13%
Advanced Statistical Process Monitoring
1.85
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