geepack

R package geepack: Generalized Estimating Equation Package. Generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. Can also handle clustered categorical responses.

Keywords for this software

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References in zbMATH (referenced in 54 articles , 1 standard article )

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  1. Sachs, M. C.; Gabriel, E. E.: Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R (2022) not zbMATH
  2. 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)
  3. Hector, Emily C.; Song, Peter X.-K.: A distributed and integrated method of moments for high-dimensional correlated data analysis (2021)
  4. Huang, Youjun; Pan, Jianxin: Joint generalized estimating equations for longitudinal binary data (2021)
  5. Kruppa, Jochen; Hothorn, Ludwig: A comparison study on modeling of clustered and overdispersed count data for multiple comparisons (2021)
  6. M. Helena Gonçalves, M. Salomé Cabral: cold: An R Package for the Analysis of Count Longitudinal Data (2021) not zbMATH
  7. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  8. Achim Zeileis, Susanne Köll, Nathaniel Graham: Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020) not zbMATH
  9. Bradley C. Saul, Michael G. Hudgens: The Calculus of M-Estimation in R with geex (2020) not zbMATH
  10. Hector, Emily C.; Song, Peter X.-K.: Doubly distributed supervised learning and inference with high-dimensional correlated outcomes (2020)
  11. Nikoloulopoulos, Aristidis K.: Weighted scores estimating equations and CL1 information criteria for longitudinal ordinal response (2020)
  12. Park, Seongoh; Lim, Johan; Choi, Hyejeong; Kwak, Minjung: Clustering of longitudinal interval-valued data via mixture distribution under covariance separability (2020)
  13. 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
  14. 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)
  15. Inan, Gul; Latif, Mahbub A. H. M.; Preisser, John: A PRESS statistic for working correlation structure selection in generalized estimating equations (2019)
  16. Pavlič, Klemen; Martinussen, Torben; Andersen, Per Kragh: Goodness of fit tests for estimating equations based on pseudo-observations (2019)
  17. Saul, Bradley C.; Hudgens, Michael G.; Mallin, Michael A.: Downstream effects of upstream causes (2019)
  18. Marchese, Scott; Diao, Guoqing: Joint regression analysis of mixed-type outcome data via efficient scores (2018)
  19. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  20. Worku, Hailemichael M.; de Rooij, Mark: A multivariate logistic distance model for the analysis of multiple binary responses (2018)

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