Multimode: An R Package for Mode Assessment

dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimizacióngl
dc.contributor.authorAmeijeiras Alonso, José
dc.contributor.authorCrujeiras Casais, Rosa María
dc.contributor.authorRodríguez Casal, Alberto
dc.date.accessioned2021-08-12T13:10:10Z
dc.date.available2021-08-12T13:10:10Z
dc.date.issued2021
dc.description.abstractIn several applied fields, multimodality assessment is a crucial task as a previous exploratory tool or for determining the suitability of certain distributions. The goal of this paper is to present the utilities of the R package multimode, which collects different exploratory and testing non-parametric approaches for determining the number of modes and their estimated location. Specifically, some graphical tools (SiZer map, mode tree or mode forest) are provided, allowing for the identification of mode patterns, based on the kernel density estimation. Several formal testing procedures for determining the number of modes are described in this paper and implemented in the multimode package, including methods based on the ideas of the critical bandwidth, the excess mass or using a combination of both. This package also includes a function for estimating the modes locations and different classical data examples that have been considered in mode testing literaturegl
dc.description.peerreviewedSIgl
dc.description.sponsorshipThe authors gratefully acknowledge the support of Project MTM2016–76969–P from the Spanish State Research Agency (AEI) co-funded by the European Regional Development Fund (ERDF), the Competitive Reference Groups 2017–2020 (ED431C 2017/38) from the Xunta de Galicia through the ERDF. Work of J. Ameijeiras-Alonso has been supported by the FWO research project G.0826.15N (Flemish Science Foundation), GOA/12/014 project (Research Fund KU Leuven) and was partially supported by the grant BES–2014–071006 from the Spanish Ministry of Science, Innovation and Universitiesgl
dc.identifier.citationJournal of Statistical Software 2021, 97(9). http://dx.doi.org/10.18637/jss.v097.i09gl
dc.identifier.doi10.18637/jss.v097.i09
dc.identifier.issn1548-7660
dc.identifier.urihttp://hdl.handle.net/10347/26799
dc.language.isoenggl
dc.publisherThe Foundation for Open Access Statisticsgl
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2016–76969–P/ESgl
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BES–2014–071006/ESgl
dc.relation.publisherversionhttp://dx.doi.org/10.18637/jss.v097.i09gl
dc.rights© 2021 by the authors. This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported Licensegl
dc.rights.accessRightsopen accessgl
dc.subjectMultimodalitygl
dc.subjectCritical bandwidthgl
dc.subjectExcess massgl
dc.subjectBootstrap testgl
dc.titleMultimode: An R Package for Mode Assessmentgl
dc.typejournal articlegl
dc.type.hasVersionVoRgl
dspace.entity.typePublication
relation.isAuthorOfPublication0fcf8811-8071-4723-a1cb-b61c69e517b8
relation.isAuthorOfPublication72f92664-9a3d-4ef9-8d09-f35c21b9454e
relation.isAuthorOfPublication91b08bcd-d203-4dad-993d-81bf0b92adda
relation.isAuthorOfPublication.latestForDiscovery0fcf8811-8071-4723-a1cb-b61c69e517b8

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