References in zbMATH (referenced in 30 articles , 1 standard article )

Showing results 1 to 20 of 30.
Sorted by year (citations)

1 2 next

  1. Bradley, Jonathan R.; Holan, Scott H.; Wikle, Christopher K.: Bayesian hierarchical models with conjugate full-conditional distributions for dependent data from the natural exponential family (2020)
  2. Matthias Speidel, Jörg Drechsler, Shahab Jolani: The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond (2020) not zbMATH
  3. Nino-Ruiz, Elias D.: A numerical method for solving linear systems in the preconditioned Crank-Nicolson algorithm (2020)
  4. Bonnet, Anna: Heritability estimation in case-control studies (2018)
  5. Bou-Rabee, Nawaf; Sanz-Serna, J. M.: Geometric integrators and the Hamiltonian Monte Carlo method (2018)
  6. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  7. Sarkar, Abhra; Chabout, Jonathan; Macopson, Joshua Jones; Jarvis, Erich D.; Dunson, David B.: Bayesian semiparametric mixed effects Markov models with application to vocalization syntax (2018)
  8. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  9. Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J.: Bayesian regression modeling with INLA (2018)
  10. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  11. Montano, Diego: Multivariate hierarchical Bayesian models and choice of priors in the analysis of survey data (2017)
  12. Ommen, Danica M.; Saunders, Christopher P.; Neumann, Cedric: The characterization of Monte Carlo errors for the quantification of the value of forensic evidence (2017)
  13. Pajala, Tommi; Korhonen, Pekka; Wallenius, Jyrki: Road to robust prediction of choices in deterministic MCDM (2017)
  14. Paul-Christian Buerkner: Bayesian Distributional Non-Linear Multilevel Modeling with the R Package brms (2017) arXiv
  15. Paul-Christian Bürkner: brms: An R Package for Bayesian Multilevel Models Using Stan (2017) not zbMATH
  16. Adelfio, Giada; Boscaino, Giovanni: Degree course change and student performance: a mixed-effect model approach (2016)
  17. 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)
  18. Russell Lenth: Least-Squares Means: The R Package lsmeans (2016) not zbMATH
  19. Zhengzheng Zhang and Richard Parker and Christopher Charlton and George Leckie and William Browne: R2MLwiN: A Package to Run MLwiN from within R (2016) not zbMATH
  20. Casals, Martí; Langohr, Klaus; Carrasco, Josep Lluís; Rönnegård, Lars: Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles: a simulation study (2015)

1 2 next