Package glmmAK: Generalized Linear Mixed Models. This package implements maximum-likelihood estimation in the logistic regression with both binary (logit model) and multinomial response (cumulative logit model), and in the Poisson regression (log-linear model). Secondly, Bayesian estimation based on MCMC in the logistic and Poisson regression model with random effects whose distribution is specified as a penalized normal mixture are implemented. The methodology is described and the package used in: KOMÁREK, A. and LESAFFRE, E. (2008). Generalized linear mixed model with a penalized Gaussian mixture as a random-effects distribution. Computational Statistics and Data Analysis, 52(7), 3441–3458,

References in zbMATH (referenced in 28 articles )

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  1. Ariyo, Oludare Samuel; Adeleke, Matthew Adekunle: Simultaneous Bayesian modelling of skew-normal longitudinal measurements with non-ignorable dropout (2022)
  2. Trong, Dang Duc; Phuong, Cao Xuan; Viet, Tran Quoc: Nonparametric estimation of random effects densities in a linear mixed-effects model with Fourier-oscillating noise density (2020)
  3. Xu, Ancha; Hu, Jiawen; Wang, Pingping: Degradation modeling with subpopulation heterogeneities based on the inverse Gaussian process (2020)
  4. Zhang, Tonglin: General Gaussian estimation (2019)
  5. Xu, Peirong; Peng, Heng; Huang, Tao: Unsupervised learning of mixture regression models for longitudinal data (2018)
  6. Cipolli, William III; Hanson, Timothy: Computationally tractable approximate and smoothed polya trees (2017)
  7. Yu, Shun; Huang, Xianzheng: Random-intercept misspecification in generalized linear mixed models for binary responses (2017)
  8. Zhang, Zhengxin; Hu, Changhua; Si, Xiaosheng; Zhang, Jianxun; Zheng, Jianfei: Stochastic degradation process modeling and remaining useful life estimation with flexible random-effects (2017)
  9. Bao, Junshu; Hanson, Timothy E.: A mean-constrained finite mixture of normals model (2016)
  10. Dalla Valle, Luciana; De Giuli, Maria Elena; Tarantola, Claudia; Manelli, Claudio: Default probability estimation via pair copula constructions (2016)
  11. Heinzl, Felix; Tutz, Gerhard: Additive mixed models with approximate Dirichlet process mixtures: the EM approach (2016)
  12. Huang, Lu; Tang, Li; Zhang, Bo; Zhang, Zhiwei; Zhang, Hui: Comparison of different computational implementations on fitting generalized linear mixed-effects models for repeated count measures (2016)
  13. Oedekoven, C. S.; King, R.; Buckland, S. T.; Mackenzie, M. L.; Evans, K. O.; Burger, L. W.: Using hierarchical centering to facilitate a reversible jump MCMC algorithm for random effects models (2016)
  14. Sauter, Rafael; Held, Leonhard: Quasi-complete separation in random effects of binary response mixed models (2016)
  15. Mabon, G.: Adaptive estimation of marginal random-effects densities in linear mixed-effects models (2015)
  16. Dion, Charlotte: New adaptive strategies for nonparametric estimation in linear mixed models (2014)
  17. Jaspers, Stijn; Aerts, Marc; Verbeke, Geert; Beloeil, Pierre-Alexandre: A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance (2014)
  18. Kauermann, Göran; Meyer, Renate: Penalized marginal likelihood estimation of finite mixtures of Archimedean copulas (2014)
  19. Gałecki, Andrzej; Burzykowski, Tomasz: Linear mixed-effects models using R. A step-by-step approach (2013)
  20. Heinzl, Felix; Tutz, Gerhard: Clustering in linear mixed models with approximate Dirichlet process mixtures using EM algorithm (2013)

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