R2WinBUGS

R2WinBUGS: Running WinBUGS and OpenBUGS from R / S-PLUS , Using this package, it is possible to call a BUGS model, summarize inferences and convergence in a table and graph, and save the simulations in arrays for easy access in R / S-PLUS. In S-PLUS, the openbugs functionality and the windows emulation functionality is not yet available. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 79 articles , 1 standard article )

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  1. Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
  2. Oǧuz-Alper, Melike; Berger, Yves G.: Modelling multilevel data under complex sampling designs: an empirical likelihood approach (2020)
  3. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  4. Conversano, Claudio; Cannas, Massimo; Mola, Francesco; Sironi, Emiliano: Random effects clustering in multilevel modeling: choosing a proper partition (2019)
  5. George G Vega Yon; Paul Marjoram: fmcmc: A friendly MCMC framework (2019) not zbMATH
  6. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  7. Li, Yong; Yu, Jun; Zeng, Tao: Hypothesis testing, specification testing, and model selection based on the MCMC output using R (2019)
  8. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  9. Islam, S.; Anand, S.; Mcqueen, M.; Hamid, J.; Thabane, L.; Yusuf, S.; Beyene, J.: Classification rules for identifying individuals at high risk of developing myocardial infarction based on ApoB, ApoA1 and the ratio were determined using a Bayesian approach (2018)
  10. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  11. Ariza-Hernandez, Francisco J.; Sanchez-Ortiz, Jorge; Arciga-Alejandre, Martin P.; Vivas-Cruz, Luis X.: Bayesian analysis for a fractional population growth model (2017)
  12. Barrado, Leandro García; Coart, Els; Burzykowski, Tomasz: Estimation of diagnostic accuracy of a combination of continuous biomarkers allowing for conditional dependence between the biomarkers and the imperfect reference-test (2017)
  13. Cannas, M.; Conversano, C.; Mola, F.; Sironi, E.: Variation in caesarean delivery rates across hospitals: a Bayesian semi-parametric approach (2017)
  14. Ganjali, M.; Moradzadeh, N.; Baghfalaki, T.: Bayesian testing of agreement criteria under order constraints (2017)
  15. Janani, Leila; Mansournia, Mohammad Ali; Mohammad, Kazem; Mahmoodi, Mahmood; Mehrabani, Kamran; Nourijelyani, Keramat: Comparison between Bayesian approach and frequentist methods for estimating relative risk in randomized controlled trials: a simulation study (2017)
  16. Mohsenkhani, Zohreh Fallah; Mohammadzadeh, Mohsen: Augmented mixed beta regression and modeling of employed proportions in households (2017)
  17. Thanoon, Thanoon Y.; Adnan, Robiah: Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data (2017)
  18. Elghafghuf, Adel; Stryhn, Henrik: Correlated versus uncorrelated frailty Cox models: a comparison of different estimation procedures (2016)
  19. Jingjing Yang, Peng Ren: BFDA: A Matlab Toolbox for Bayesian Functional Data Analysis (2016) arXiv
  20. Shi, Peng; Hartman, Brian M.: Credibility in loss reserving (2016)

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