Rainbow

R package rainbow: Rainbow Plots, Bagplots and Boxplots for Functional Data, Functions and data sets for functional data display and outlier detection.

Keywords for this software

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References in zbMATH (referenced in 46 articles )

Showing results 1 to 20 of 46.
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  1. Beyaztas, Ufuk; Shang, Han Lin: A robust partial least squares approach for function-on-function regression (2022)
  2. Elías, Antonio; Jiménez, Raúl; Shang, Han Lin: On projection methods for functional time series forecasting (2022)
  3. Yang, Yang; Yang, Yanrong; Shang, Han Lin: Feature extraction for functional time series: theory and application to NIR spectroscopy data (2022)
  4. Hsieh, I-Chung; Huang, Yufen: Sensitivity analysis and visualization for functional data (2021)
  5. Kolkiewicz, Adam; Rice, Gregory; Xie, Yijun: Projection pursuit based tests of normality with functional data (2021)
  6. Mestre, Guillermo; Portela, José; Rice, Gregory; Muñoz San Roque, Antonio; Alonso, Estrella: Functional time series model identification and diagnosis by means of auto- and partial autocorrelation analysis (2021)
  7. Oluwasegun Ojo, Rosa E. Lillo, Antonio Fernández Anta: Outlier Detection for Functional Data with R Package fdaoutlier (2021) arXiv
  8. Shang, Han Lin: Double bootstrapping for visualizing the distribution of descriptive statistics of functional data (2021)
  9. Vinue, Guillermo; Epifanio, Irene: Robust archetypoids for anomaly detection in big functional data (2021)
  10. Chen, Yi-Ting; Tsay, Ruey S.: Time evolution of income distributions with subgroup decompositions (2020)
  11. Dai, Wenlin; Mrkvička, Tomáš; Sun, Ying; Genton, Marc G.: Functional outlier detection and taxonomy by sequential transformations (2020)
  12. Ieva, Francesca; Paganoni, Anna Maria: Component-wise outlier detection methods for robustifying multivariate functional samples (2020)
  13. Shang, Han Lin: Estimation of a functional single index model with dependent errors and unknown error density (2020)
  14. Dai, Wenlin; Genton, Marc G.: Directional outlyingness for multivariate functional data (2019)
  15. Kosiorowski, Daniel; Rydlewski, Jerzy P.; Snarska, Małgorzata: Detecting a structural change in functional time series using local Wilcoxon statistic (2019)
  16. Martínez-Hernández, Israel; Genton, Marc G.; González-Farías, Graciela: Robust depth-based estimation of the functional autoregressive model (2019)
  17. Moliner, Jesús; Epifanio, Irene: Robust multivariate and functional archetypal analysis with application to financial time series analysis (2019)
  18. Nagy, Stanislav; Ferraty, Frédéric: Data depth for measurable noisy random functions (2019)
  19. Shang, Han Lin: Dynamic principal component regression: application to age-specific mortality forecasting (2019)
  20. Shang, Han Lin; Yang, Yang; Kearney, Fearghal: Intraday forecasts of a volatility index: functional time series methods with dynamic updating (2019)

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