zoo

R package zoo: S3 Infrastructure for Regular and Irregular Time Series (Z’s ordered observations). An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo’s key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.


References in zbMATH (referenced in 48 articles , 1 standard article )

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  1. Jeffrey W. Hollister, Dorothy Q. Kellogg, Qian Lei-Parent, Emily Wilson, Cary Chadwick, David Dickson, Arthur Gold, Chester Arnold: nsink: An R package for flow path nitrogen removal estimation (2022) not zbMATH
  2. Adithi R. Upadhya, Pratyush Agrawal, Sreekanth Vakacherla, Meenakshi Kushwaha: pollucheck v1.0: A package to explore open-source air pollution data (2021) not zbMATH
  3. Ben O’Neill: Gaussian ARMA models in the ts.extend package (2021) arXiv
  4. David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt: The R package sentometrics to compute, aggregate and predict with textual sentiment (2021) arXiv
  5. Hosszejni, D.; Kastner, G: Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol (2021) not zbMATH
  6. Klaus Nordhausen, Markus Matilainen, Jari Miettinen, Joni Virta, Sara Taskinen: Dimension Reduction for Time Series in a Blind Source Separation Context Using R (2021) not zbMATH
  7. Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
  8. Samuel Borms, David Ardia, Keven Bluteau, Kris Boudt, Jeroen Van Pelt, Andres Algaba: The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment (2021) not zbMATH
  9. Claudia Cappello, Sandra De Iaco, Donato Posa: covatest: An R Package for Selecting a Class of Space-Time Covariance Functions (2020) not zbMATH
  10. Daniel Peña, Ezequiel Smucler, Victor Yohai: gdpc: An R Package for Generalized Dynamic Principal Components (2020) not zbMATH
  11. Lisa Amrhein, Christiane Fuchs: stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R (2020) arXiv
  12. Martinovič, T.: Alternative approaches of evaluating the (0-1) test for chaos (2020)
  13. Nicholas J Tierney, Dianne Cook, Tania Prvan: brolgar: An R package to BRowse Over Longitudinal Data Graphically and Analytically in R (2020) arXiv
  14. Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP (2019) arXiv
  15. Annette Möller, Jürgen Groß: Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model (2019) arXiv
  16. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  17. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  18. Kocbek, Primoz; Fijacko, Nino; Soguero-Ruiz, Cristina; Mikalsen, Karl Øyvind; Maver, Uros; Povalej Brzan, Petra; Stozer, Andraz; Jenssen, Robert; Skrøvseth, Stein Olav; Stiglic, Gregor: Maximizing interpretability and cost-effectiveness of surgical site infection (SSI) predictive models using feature-specific regularized logistic regression on preoperative temporal data (2019)
  19. Victor Maus and Gilberto Câmara and Marius Appel and Edzer Pebesma: dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R (2019) not zbMATH
  20. Felix Pretis; J. Reade; Genaro Sucarrat: Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks (2018) not zbMATH

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