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 86 articles , 1 standard article )

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  1. Francisco Palmí-Perales, Virgilio Gómez-Rubio, Miguel A. Martinez-Beneito: Bayesian Multivariate Spatial Models for Lattice Data with INLA (2021) not zbMATH
  2. Hu, Jinxiang; Clark, Lauren; Shi, Peng; Staggs, Vincent S.; Daley, Christine; Gajewski, Byron: Bayesian hierarchical factor analysis for eficient estimation across race/ethnicity (2021)
  3. Perry de Valpine, Sally Paganin, Daniel Turek: compareMCMCs: An R package for studying MCMC efficiency (2021) not zbMATH
  4. Rosner, Gary L.; Laud, Purushottam W.; Johnson, Wesley O.: Bayesian thinking in biostatistics (2021)
  5. Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
  6. Wu, Qian; Vanerum, Monique; Agten, Anouk; Christiansen, Andrés; Vandenabeele, Frank; Rigo, Jean-Michel; Janssen, Rianne: Certainty-based marking on multiple-choice items: psychometrics meets decision theory (2021)
  7. Farzammehr, Mohadeseh Alsadat; Zadkarami, Mohammad Reza; McLachlan, Geoffrey J.; Lee, Sharon X.: Skew-normal Bayesian spatial heterogeneity panel data models (2020)
  8. Oǧuz-Alper, Melike; Berger, Yves G.: Modelling multilevel data under complex sampling designs: an empirical likelihood approach (2020)
  9. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  10. Conversano, Claudio; Cannas, Massimo; Mola, Francesco; Sironi, Emiliano: Random effects clustering in multilevel modeling: choosing a proper partition (2019)
  11. George G Vega Yon; Paul Marjoram: fmcmc: A friendly MCMC framework (2019) not zbMATH
  12. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  13. Li, Yong; Yu, Jun; Zeng, Tao: Hypothesis testing, specification testing, and model selection based on the MCMC output using R (2019)
  14. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  15. 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)
  16. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  17. Khan, Shahedul A.; Kar, Setu C.: Generalized bent-cable methodology for changepoint data: a Bayesian approach (2018)
  18. Ariza-Hernandez, Francisco J.; Sanchez-Ortiz, Jorge; Arciga-Alejandre, Martin P.; Vivas-Cruz, Luis X.: Bayesian analysis for a fractional population growth model (2017)
  19. 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)
  20. Cannas, M.; Conversano, C.; Mola, F.; Sironi, E.: Variation in caesarean delivery rates across hospitals: a Bayesian semi-parametric approach (2017)

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