R package quantreg: Quantile Regression. Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also included. (Source: http://cran.r-project.org/web/packages)

References in zbMATH (referenced in 76 articles )

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  1. Fan, Yali; Tang, Yanlin; Zhu, Zhongyi: Variable selection in censored quantile regression with high dimensional data (2018)
  2. Gregory, Karl B.; Lahiri, Soumendra N.; Nordman, Daniel J.: A smooth block bootstrap for quantile regression with time series (2018)
  3. Zheng, Qi; Peng, Limin; He, Xuming: High dimensional censored quantile regression (2018)
  4. Abdelaati Daouia and Thibault Laurent and Hohsuk Noh: npbr: A Package for Nonparametric Boundary Regression in R (2017)
  5. Boček, Pavel; Šiman, Miroslav: On weighted and locally polynomial directional quantile regression (2017)
  6. Chesher, Andrew: Understanding the effect of measurement error on quantile regressions (2017)
  7. Díaz, Iván: Efficient estimation of quantiles in missing data models (2017)
  8. Dries Benoit and Dirk Van den Poel: bayesQR: A Bayesian Approach to Quantile Regression (2017)
  9. Goldman, Matt; Kaplan, David M.: Fractional order statistic approximation for nonparametric conditional quantile inference (2017)
  10. Maistre, Samuel; Lavergne, Pascal; Patilea, Valentin: Powerful nonparametric checks for quantile regression (2017)
  11. Matthew Pietrosanu, Jueyu Gao, Linglong Kong, Bei Jiang, Di Niu: cqrReg: An R Package for Quantile and Composite Quantile Regression and Variable Selection (2017) arXiv
  12. Xu, Guanglin; Burer, Samuel: A branch-and-bound algorithm for instrumental variable quantile regression (2017)
  13. Zhou, Haiming; Hanson, Timothy; Zhang, Jiajia: Generalized accelerated failure time spatial frailty model for arbitrarily censored data (2017)
  14. Anita Thieler; Roland Fried; Jonathan Rathjens: RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression (2016)
  15. Fuzi, Mohd Fadzli Mohd; Jemain, Abdul Aziz; Ismail, Noriszura: Bayesian quantile regression model for claim count data (2016)
  16. Galvao, Antonio F.; Kato, Kengo: Smoothed quantile regression for panel data (2016)
  17. Mingli Chen, Victor Chernozhukov, Ivan Fernandez-Val, Blaise Melly: Counterfactual: An R Package for Counterfactual Analysis (2016) arXiv
  18. Park, Young Woong; Klabjan, Diego: An aggregate and iterative disaggregate algorithm with proven optimality in machine learning (2016)
  19. Pieter Schoonees and Niël le Roux and Roelof Coetzer: Flexible Graphical Assessment of Experimental Designs in R: The vdg Package (2016)
  20. Tobias Kley: Quantile-Based Spectral Analysis in an Object-Oriented Framework and a Reference Implementation in R: The quantspec Package (2016)

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