rjags: Bayesian graphical models using MCMC. Interface to the JAGS MCMC library. The rjags package provides an interface from R to the JAGS library for Bayesian data analysis. JAGS uses Markov Chain Monte Carlo (MCMC) to generate a sequence of dependent samples from the posterior distribution of the parameters .

References in zbMATH (referenced in 22 articles )

Showing results 1 to 20 of 22.
Sorted by year (citations)

1 2 next

  1. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  2. Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers: bridgesampling: An R Package for Estimating Normalizing Constants (2017) arXiv
  3. Arcuti, Simona; Pollice, Alessio; Ribecco, Nunziata; D’Onghia, Gianfranco: Bayesian spatiotemporal analysis of zero-inflated biological population density data by a delta-normal spatiotemporal additive model (2016)
  4. Fernandes, Laura L.; Murray, Susan; Taylor, Jeremy M.G.: Multivariate Markov models for the conditional probability of toxicity in phase II trials (2016)
  5. King, Ruth; McClintock, Brett T.; Kidney, Darren; Borchers, David: Capture-recapture abundance estimation using a semi-complete data likelihood approach (2016)
  6. Matthew Denwood: runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS (2016)
  7. Röver, Christian; Andreas, Stefan; Friede, Tim: Evidence synthesis for count distributions based on heterogeneous and incomplete aggregated data (2016)
  8. Shaby, Benjamin A.; Skibinski, Gaia; Ando, Michael; Ladow, Eva S.; Finkbeiner, Steven: A three-groups model for high-throughput survival screens (2016)
  9. Simon Wood: Just Another Gibbs Additive Modeler: Interfacing JAGS and mgcv (2016)
  10. Anders, Royce; Batchelder, William H.: Cultural consensus theory for the ordinal data case (2015)
  11. Cahill, Niamh; Kemp, Andrew C.; Horton, Benjamin P.; Parnell, Andrew C.: Modeling sea-level change using errors-in-variables integrated Gaussian processes (2015)
  12. Khandoker Bakar; Sujit Sahu: spTimer: Spatio-Temporal Bayesian Modeling Using R (2015)
  13. Oravecz, Zita; Anders, Royce; Batchelder, William H.: Hierarchical Bayesian modeling for test theory without an answer key (2015)
  14. Adrien Todeschini, Francois Caron, Marc Fuentes, Pierrick Legrand, Pierre Del Moral: Biips: Software for Bayesian Inference with Interacting Particle Systems (2014) arXiv
  15. Anders, R.; Oravecz, Z.; Batchelder, W.H.: Cultural consensus theory for continuous responses: a latent appraisal model for information pooling (2014)
  16. Chris Wheadon: Classification Accuracy and Consistency under Item Response Theory Models Using the Package classify (2014)
  17. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  18. Zhang, Jingyang; Brown, Elizabeth R.: Estimating the effectiveness in HIV prevention trials by incorporating the exposure process: application to HPTN 035 data (2014)
  19. Bonner, Simon J.; Holmberg, Jason: Mark-recapture with multiple, non-invasive marks (2013)
  20. Janicki, Ryan; Malec, Donald: A Bayesian model averaging approach to analyzing categorical data with nonignorable nonresponse (2013)

1 2 next