CODA

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: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 190 articles )

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  1. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  2. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
  3. Gregg, Robert W.; Sarkar, Saumendra N.; Shoemaker, Jason E.: Mathematical modeling of the cGAS pathway reveals robustness of DNA sensing to TREX1 feedback (2019)
  4. Johnson, Margaret; Caragea, Petruţa C.; Meiring, Wendy; Jeganathan, C.; Atkinson, Peter M.: Bayesian dynamic linear models for estimation of phenological events from remote sensing data (2019)
  5. Malcolm S. Itter, Jarno Vanhatalo, Andrew O. Finley: EcoMem: An R package for quantifying ecological memory (2019) arXiv
  6. Osthus, Dave; Gattiker, James; Priedhorsky, Reid; Del Valle, Sara Y.: Dynamic Bayesian influenza forecasting in the United States with hierarchical discrepancy (with discussion) (2019)
  7. Rizzo, Maria L.: Statistical computing with R (2019)
  8. Boehm, Udo; Annis, Jeffrey; Frank, Michael J.; Hawkins, Guy E.; Heathcote, Andrew; Kellen, David; Krypotos, Angelos-Miltiadis; Lerche, Veronika; Logan, Gordon D.; Palmeri, Thomas J.; van Ravenzwaaij, Don; Servant, Mathieu; Singmann, Henrik; Starns, Jeffrey J.; Voss, Andreas; Wiecki, Thomas V.; Matzke, Dora; Wagenmakers, Eric-Jan: Estimating across-trial variability parameters of the diffusion decision model: expert advice and recommendations (2018)
  9. Chanialidis, Charalampos; Evers, Ludger; Neocleous, Tereza; Nobile, Agostino: Efficient Bayesian inference for COM-Poisson regression models (2018)
  10. Chitakasempornkul, Kessinee; Sanderson, Michael W.; Cha, Elva; Renter, David G.; Jager, Abigail; Bello, Nora M.: Accounting for data architecture on structural equation modeling of feedlot cattle performance (2018)
  11. Chris Groendyke; David Welch: epinet: An R Package to Analyze Epidemics Spread across Contact Networks (2018) not zbMATH
  12. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  13. Depaoli, Sarah; Liu, Yang: Book review of: R. Levy and R. J. Mislevy, Bayesian psychometric modeling (2018)
  14. Drovandi, Christopher C.; Moores, Matthew T.; Boys, Richard J.: Accelerating pseudo-marginal MCMC using Gaussian processes (2018)
  15. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  16. Erbisti, Rafael S.; Fonseca, Thais C. O.; Alves, Mariane B.: Covariance modeling for multivariate spatial processes based on separable approximations (2018)
  17. Georgios Papageorgiou: BNSP: an R Package for Fitting Bayesian Regression Models With Semiparametric Mean and Variance Functions (2018) arXiv
  18. Gómez-Rubio, Virgilio; Rue, Håvard: Markov chain Monte Carlo with the integrated nested Laplace approximation (2018)
  19. Kamatani, Kengo: Efficient strategy for the Markov chain Monte Carlo in high-dimension with heavy-tailed target probability distribution (2018)
  20. Leisen, Fabrizio; Rossini, Luca; Villa, Cristiano: Objective Bayesian analysis of the Yule-Simon distribution with applications (2018)

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