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 38 articles , 1 standard article )

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  1. Claudia Cappello, Sandra De Iaco, Donato Posa: covatest: An R Package for Selecting a Class of Space-Time Covariance Functions (2020) not zbMATH
  2. Daniel Peña, Ezequiel Smucler, Victor Yohai: gdpc: An R Package for Generalized Dynamic Principal Components (2020) not zbMATH
  3. Lisa Amrhein, Christiane Fuchs: stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R (2020) arXiv
  4. Nicholas J Tierney, Dianne Cook, Tania Prvan: brolgar: An R package to BRowse Over Longitudinal Data Graphically and Analytically in R (2020) arXiv
  5. 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
  6. Annette Möller, Jürgen Groß: Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model (2019) arXiv
  7. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  8. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  9. 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)
  10. 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
  11. Felix Pretis; J. Reade; Genaro Sucarrat: Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks (2018) not zbMATH
  12. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  13. Simon A. C. Taylor; Timothy Park; Idris A. Eckley: Multivariate Locally Stationary Wavelet Process Analysis with the mvLSW R Package (2018) arXiv
  14. Stübinger, Johannes; Endres, Sylvia: Pairs trading with a mean-reverting jump-diffusion model on high-frequency data (2018)
  15. Stübinger, Johannes; Mangold, Benedikt; Krauss, Christopher: Statistical arbitrage with vine copulas (2018)
  16. Amélie Anota and Marion Savina and Caroline Bascoul-Mollevi and Franck Bonnetain: QoLR: An R Package for the Longitudinal Analysis of Health-Related Quality of Life in Oncology (2017) not zbMATH
  17. Hannah Frick; Ioannis Kosmidis: trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R (2017) not zbMATH
  18. Howard, James P. II: Computational methods for numerical analysis with R (2017)
  19. Maëlle Salmon; Dirk Schumacher; Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance (2016) not zbMATH
  20. Neeraj Bokde, Kishore Kulat, Marcus W Beck, Gualberto Asencio-Cortes: R package imputeTestbench to compare imputations methods for univariate time series (2016) arXiv

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