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dc.contributor.authorSolvang, Hiroko Kato
dc.contributor.authorOhishi, Mineaki
dc.date.accessioned2023-09-25T11:33:11Z
dc.date.available2023-09-25T11:33:11Z
dc.date.created2023-02-18T19:52:14Z
dc.date.issued2023
dc.identifier.citationSoftwareX. 2023, 21 .en_US
dc.identifier.issn2352-7110
dc.identifier.urihttps://hdl.handle.net/11250/3091758
dc.description.abstractSolvang and Planque [ICES Journal of Marine Science, 77, pp.2529–2540, (2020)] provided a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. This approach was developed to improve communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies as they investigate the common tendencies between a biological community in a marine ecosystem and the local environmental factors. The tasks of trend estimation and classification in the original computational procedure have been revised, and new features include an automatic icon assignment algorithm using a multinomial logistic discriminator. In this paper, we present R package trec. Implementation of this package involves three partitions corresponding to TREC1) estimating trends from observed time series data; TREC2) classifying two/three rough patterns; and TREC3) generating a table summarizing categories of common configurations (trends) and the automatic icon assignments to them. The proposed trec focuses on investigating mean non-stationary long-term trends of data, and it works for any length of time steps. It is not necessary to apply a stationary Gaussian assumption to the estimated trends to investigate the common trends, which are interpreted as common variations of biological and environmental data.en_US
dc.language.isoengen_US
dc.titletrec: An R package for trend estimation and classification to support integrated ecosystem assessment of the marine ecosystem and environmental factorsen_US
dc.title.alternativetrec: An R package for trend estimation and classification to support integrated ecosystem assessment of the marine ecosystem and environmental factorsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber7en_US
dc.source.volume21en_US
dc.source.journalSoftwareXen_US
dc.identifier.doi10.1016/j.softx.2023.101309
dc.identifier.cristin2127256
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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