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Statistical Quality Control with the qcr Package
dc.contributor.author | Flores, Miguel | |
dc.contributor.author | Fernández-Casal, Rubén | |
dc.contributor.author | Naya, Salvador | |
dc.contributor.author | Tarrío-Saavedra, Javier | |
dc.date.accessioned | 2021-12-30T10:30:04Z | |
dc.date.available | 2021-12-30T10:30:04Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | FLORES, M., FERNÁNDEZ-CASAL, R., NAYA, S. and TARRÍO-SAAVEDRA, J., 2021. Statistical Quality Control with the qcr Package. R Journal. 2021. Vol. 13, no. 1, p. 194–217. DOI 10.32614/rj-2021-034 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/29279 | |
dc.description.abstract | [Abstract] The R package qcr for Statistical Quality Control (SQC) is introduced and described. It includes a comprehensive set of univariate and multivariate SQC tools that completes and increases the SQC techniques available in R. Apart from integrating different R packages devoted to SQC (qcc, MSQC), qcr provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, (Formula presented), S, R, p, np, c, u, EWMA, and CUSUM. In addition, it includes functions to perform multivariate control charts such as Hotelling T2, MEWMA and MCUSUM. As representative features, multivariate nonparametric alternatives based on data depth are implemented in this package: r, Q and S control charts. The qcr library also estimates the most complete set of capability indices from first to the fourth generation, covering the nonparametric alternatives, and performing the corresponding capability analysis graphical outputs, including the process capability plots. Moreover, Phase I and II control charts for functional data are included. | es_ES |
dc.description.sponsorship | The work of Salvador Naya, Javier Tarrío-Saavedra, Miguel Flores and Rubén Fernández-Casal has been supported by MINECO grant MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema universitario de Galicia ED431G 2019/01), all of them through the ERDF. The research of Miguel Flores has been partially supported by Grant PII-DM-002-2016 of Escuela Politécnica Nacional of Ecuador. In addition, the research of Javier Tarrío-Saavedra has been also founded by the eCOAR project (PC18/03) of CITIC | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Escuela Politécnica Nacional de Ecuador; PII-DM-002-2016 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Technische Universitaet Wien | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | |
dc.relation.uri | http://doi.org/10.32614/rj-2021-034 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | Statistical Quality Control with the qcr Package | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | R Journal | es_ES |
UDC.volume | 13 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 194 | es_ES |
UDC.endPage | 217 | es_ES |
dc.identifier.doi | 10.32614/rj-2021-034 |
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