References in zbMATH (referenced in 57 articles , 1 standard article )

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  1. Diop, S. Arona; Duchesne, Thierry; Cumming, Steven G.; Diop, Awa; Talbot, Denis: Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification (2022)
  2. Sachs, M. C.; Gabriel, E. E.: Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R (2022) not zbMATH
  3. Elliott, Corrine F.; Lambert, Joshua W.; Stromberg, Arnold J.; Wang, Pei; Zeng, Ting; Thompson, Katherine L.: Feasibility as a mechanism for model identification and validation (2021)
  4. Hector, Emily C.; Song, Peter X.-K.: A distributed and integrated method of moments for high-dimensional correlated data analysis (2021)
  5. Huang, Youjun; Pan, Jianxin: Joint generalized estimating equations for longitudinal binary data (2021)
  6. Kruppa, Jochen; Hothorn, Ludwig: A comparison study on modeling of clustered and overdispersed count data for multiple comparisons (2021)
  7. Liya, Fu; Yang, Zhuoran; Zhang, Jun; Long, Anle; Zhou, Yan: Generalized estimating equations for analyzing multivariate survival data (2021)
  8. M. Helena Gonçalves, M. Salomé Cabral: cold: An R Package for the Analysis of Count Longitudinal Data (2021) not zbMATH
  9. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  10. Achim Zeileis, Susanne Köll, Nathaniel Graham: Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020) not zbMATH
  11. Bradley C. Saul, Michael G. Hudgens: The Calculus of M-Estimation in R with geex (2020) not zbMATH
  12. Hector, Emily C.; Song, Peter X.-K.: Doubly distributed supervised learning and inference with high-dimensional correlated outcomes (2020)
  13. Nikoloulopoulos, Aristidis K.: Weighted scores estimating equations and CL1 information criteria for longitudinal ordinal response (2020)
  14. Park, Seongoh; Lim, Johan; Choi, Hyejeong; Kwak, Minjung: Clustering of longitudinal interval-valued data via mixture distribution under covariance separability (2020)
  15. Tracie L. Shing, John S. Preisser, Richard C. Zink: GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations (2020) arXiv
  16. da Silva, José L. P.; Colosimo, Enrico A.; Demarqui, Fábio N.: A general GEE framework for the analysis of longitudinal ordinal missing data and related issues (2019)
  17. Inan, Gul; Latif, Mahbub A. H. M.; Preisser, John: A PRESS statistic for working correlation structure selection in generalized estimating equations (2019)
  18. Pavlič, Klemen; Martinussen, Torben; Andersen, Per Kragh: Goodness of fit tests for estimating equations based on pseudo-observations (2019)
  19. Saul, Bradley C.; Hudgens, Michael G.; Mallin, Michael A.: Downstream effects of upstream causes (2019)
  20. Xu, Cong; Li, Zheng; Xue, Yuan; Zhang, Lijun; Wang, Ming: An R package for model fitting, model selection and the simulation for longitudinal data with dropout missingness (2019)

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