R package coda: Output analysis and diagnostics for MCMC , Output analysis and diagnostics for Markov Chain Monte Carlo simulations. Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain. (Source:

References in zbMATH (referenced in 150 articles )

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  1. Chanialidis, Charalampos; Evers, Ludger; Neocleous, Tereza; Nobile, Agostino: Efficient Bayesian inference for COM-Poisson regression models (2018)
  2. Chris Groendyke; David Welch: epinet: An R Package to Analyze Epidemics Spread across Contact Networks (2018)
  3. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  4. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018)
  5. Georgios Papageorgiou: BNSP: an R Package for Fitting Bayesian Regression Models With Semiparametric Mean and Variance Functions (2018) arXiv
  6. Kamatani, Kengo: Efficient strategy for the Markov chain Monte Carlo in high-dimension with heavy-tailed target probability distribution (2018)
  7. Leisen, Fabrizio; Rossini, Luca; Villa, Cristiano: Objective Bayesian analysis of the Yule-Simon distribution with applications (2018)
  8. Mastrantonio, Gianluca: The joint projected normal and skew-normal: a distribution for poly-cylindrical data (2018)
  9. Andrianakis, Ioannis; McCreesh, Nicky; Vernon, Ian; McKinley, Trevelyan J.; Oakley, Jeremy E.; Nsubuga, Rebecca N.; Goldstein, Michael; White, Richard G.: Efficient history matching of a high dimensional individual-based HIV transmission model (2017)
  10. Antonio Canale: msBP: An R Package to Perform Bayesian Nonparametric Inference Using Multiscale Bernstein Polynomials Mixtures (2017)
  11. Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell: Stan: A Probabilistic Programming Language (2017)
  12. Chen Dong; Michel Wedel: BANOVA: An R Package for Hierarchical Bayesian ANOVA (2017)
  13. Davies, Vinny; Reeve, Richard; Harvey, William T.; Maree, Francois F.; Husmeier, Dirk: A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution (2017)
  14. Dries Benoit and Dirk Van den Poel: bayesQR: A Bayesian Approach to Quantile Regression (2017)
  15. Fox, Jean-Paul; Mulder, Joris; Sinharay, Sandip: Bayes factor covariance testing in item response models (2017)
  16. Gamado, Kokouvi; Streftaris, George; Zachary, Stan: Estimation of under-reporting in epidemics using approximations (2017)
  17. Gronau, Quentin F.; Sarafoglou, Alexandra; Matzke, Dora; Ly, Alexander; Boehm, Udo; Marsman, Maarten; Leslie, David S.; Forster, Jonathan J.; Wagenmakers, Eric-Jan; Steingroever, Helen: A tutorial on bridge sampling (2017)
  18. Jones, Graham: Algorithmic improvements to species delimitation and phylogeny estimation under the multispecies coalescent (2017)
  19. Khadraoui, Khader: Nonparametric adaptive Bayesian regression using priors with tractable normalizing constants and under qualitative assumptions (2017)
  20. Lahoz-Monfort, José J.; Harris, Michael P.; Wanless, Sarah; Freeman, Stephen N.; Morgan, Byron J. T.: Bringing it all together: multi-species integrated population modelling of a breeding community (2017)

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