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dc.contributor.authorTusell Palmer, Fernando Jorge
dc.date.accessioned2014-02-21T18:29:29Z
dc.date.available2014-02-21T18:29:29Z
dc.date.issued2011-03
dc.identifier.citationJournal of Statistical Software 39(2) : 1-27 (2011)es
dc.identifier.issn1548-7660
dc.identifier.urihttp://hdl.handle.net/10810/11607
dc.description.abstractSupport in R for state space estimation via Kalman filtering was limited to one package, until fairly recently. In the last five years, the situation has changed with no less than four additional packages offering general implementations of the Kalman filter, including in some cases smoothing, simulation smoothing and other functionality. This paper reviews some of the offerings in R to help the prospective user to make an informed choice.es
dc.description.sponsorshipPartial support from grants ECO2008-05622 (MCyT) and IT-347-10 (Basque Government)es
dc.language.isoenges
dc.publisherJournal of Statistical Softwarees
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.subjectstate space modelses
dc.subjectKalman filteres
dc.subjecttime serieses
dc.subjectRes
dc.titleKalman Filtering in Res
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holderis work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3)es
dc.relation.publisherversionhttp://www.jstatsoft.org/v39/i02es
dc.departamentoesEconomía aplicada III (Econometría y Estadística)es_ES
dc.departamentoeuEkonomia aplikatua III (ekonometria eta estatistika)es_ES
dc.subject.categoriaSOFTWARE
dc.subject.categoriaSTATISTICS AND PROBABILITY


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