dc.creatorFachada N
dc.creatorRodrigues J
dc.creatorLopes V.V
dc.creatorMartins R.C
dc.creatorRosa A.C.
dc.date.accessioned2021-03-17T18:06:34Z
dc.date.available2021-03-17T18:06:34Z
dc.date.created2021-03-17T18:06:34Z
dc.date.issued2016-11-21
dc.identifier2073-4859
dc.identifierhttps://hdl.handle.net/20.500.12815/214
dc.identifierhttps://doi.org/10.32614/rj-2016-055
dc.identifierThe R Journal
dc.description.abstractThe R package micompr implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution. The procedure uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar high-dimension multivariate observations.
dc.languageeng
dc.publisherThe R Foundation for Statistical Computing
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceRepositorio Institucional UTEC
dc.sourceUniversidad de Ingeniería y Tecnología - UTEC
dc.subjectMicompr
dc.subjectvegan
dc.subjectBlossom
dc.subjectEnergy
dc.subjectCrossmatch
dc.subjectCramer
dc.subjectChemoSpec
dc.subjectBiotools
dc.subjectTestthat
dc.subjectKnitr
dc.subjectRoxygen2
dc.subjectDeseasonalize
dc.titleMicompr: An r package for multivariate independent comparison of observations
dc.typeinfo:eu-repo/semantics/article


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