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 194 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. Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP (2019) arXiv
  4. Gregg, Robert W.; Sarkar, Saumendra N.; Shoemaker, Jason E.: Mathematical modeling of the cGAS pathway reveals robustness of DNA sensing to TREX1 feedback (2019)
  5. João Duarte; Vinícius Mayrink: slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis (2019) not zbMATH
  6. 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)
  7. Malcolm S. Itter, Jarno Vanhatalo, Andrew O. Finley: EcoMem: An R package for quantifying ecological memory (2019) arXiv
  8. Osthus, Dave; Gattiker, James; Priedhorsky, Reid; Del Valle, Sara Y.: Dynamic Bayesian influenza forecasting in the United States with hierarchical discrepancy (with discussion) (2019)
  9. Rizzo, Maria L.: Statistical computing with R (2019)
  10. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  11. Ziwen An, Leah F. South, Christopher C. Drovand: BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood (2019) arXiv
  12. 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)
  13. Chanialidis, Charalampos; Evers, Ludger; Neocleous, Tereza; Nobile, Agostino: Efficient Bayesian inference for COM-Poisson regression models (2018)
  14. 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)
  15. Chris Groendyke; David Welch: epinet: An R Package to Analyze Epidemics Spread across Contact Networks (2018) not zbMATH
  16. Cowles, Mary Kathryn; Bonett, Stephen; Seedorff, Michael: Independent sampling for Bayesian normal conditional autoregressive models with OpenCL acceleration (2018)
  17. Depaoli, Sarah; Liu, Yang: Book review of: R. Levy and R. J. Mislevy, Bayesian psychometric modeling (2018)
  18. Drovandi, Christopher C.; Moores, Matthew T.; Boys, Richard J.: Accelerating pseudo-marginal MCMC using Gaussian processes (2018)
  19. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  20. Erbisti, Rafael S.; Fonseca, Thais C. O.; Alves, Mariane B.: Covariance modeling for multivariate spatial processes based on separable approximations (2018)

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