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zoo: S3 Infrastructure for Regular and Irregular Time Series

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Author Info

  • Achim Zeileis
  • Gabor Grothendieck

Abstract

zoo is an R package providing an S3 class with methods for indexed totally ordered observations, such as discrete irregular time series. Its key design goals are independence of a particular index/time/date class and consistency with base R and the "ts" class for regular time series. This paper describes how these are achieved within zoo and provides several illustrations of the available methods for "zoo" objects which include plotting, merging and binding, several mathematical operations, extracting and replacing data and index, coercion and NA handling. A subclass "zooreg" embeds regular time series into the "zoo" framework and thus bridges the gap between regular and irregular time series classes in R.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Statistical Software.

Volume (Year): 14 ()
Issue (Month): i06 ()
Pages:

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Handle: RePEc:jss:jstsof:14:i06

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Web page: http://www.jstatsoft.org/

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Cited by:
  1. Robert Ferstl & Josef Hayden, . "Zero-Coupon Yield Curve Estimation with the Package termstrc," Journal of Statistical Software, American Statistical Association, vol. 36(i01).
  2. Matthias Bannert, 2013. "gateveys – An R Toolbox for Re-traceable Aggregation of Business Tendency Survey Data," KOF Working papers 13-326, KOF Swiss Economic Institute, ETH Zurich.
  3. Jiří Sedláček, 2013. "Using R in Finance," Český finanční a účetní časopis, University of Economics, Prague, vol. 2013(4), pages 145-163.
  4. Garrett Grolemund & Hadley Wickham, . "Dates and Times Made Easy with lubridate," Journal of Statistical Software, American Statistical Association, vol. 40(i03).
  5. Nikolaus Umlauf & Georg Mayr & Jakob Messner & Achim Zeileis, 2011. "Why Does It Always Rain on Me? A Spatio-Temporal Analysis of Precipitation in Austria," Working Papers 2011-25, Faculty of Economics and Statistics, University of Innsbruck.
  6. Mirko Seithe & Lena Calahorrano, 2014. "Analysing Party Preferences Using Google Trends," CESifo Working Paper Series 4631, CESifo Group Munich.
  7. Golyandina, Nina & Korobeynikov, Anton, 2014. "Basic Singular Spectrum Analysis and forecasting with R," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 934-954.
  8. Peter Egger & Sergey Nigai, 2013. "Energy Reform in Switzerland: A Quantification of Carbon Taxation and Nuclear Energy Substitution Effects," KOF Working papers 13-327, KOF Swiss Economic Institute, ETH Zurich.
  9. A. Ian McLeod & Ying Zhang, . "Improved Subset Autoregression: With R Package," Journal of Statistical Software, American Statistical Association, vol. 28(i02).

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