rjags

R package 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.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 64 articles )

Showing results 1 to 20 of 64.
Sorted by year (citations)

1 2 3 4 next

  1. Manderson, Andrew A.; Goudie, Robert J. B.: A numerically stable algorithm for integrating Bayesian models using Markov melding (2022)
  2. Nathan Green, Anna Heath, Gianluca Baio: BCEA: An R Package for Cost-Effectiveness Analysis (2022) arXiv
  3. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
  4. Bonner, S., Kim, H.-N., Westneat, D., Mutzel, A., Wright, J., Schofield, M.: dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble (2021) not zbMATH
  5. Brandon P.M. Edwards, Adam C. Smith: bbsBayes: An R Package for Hierarchical Bayesian Analysis of North American Breeding Bird Survey Data (2021) not zbMATH
  6. Chao, Fengqing; Gerland, Patrick; Cook, Alex R.; Alkema, Leontine: Global estimation and scenario-based projections of sex ratio at birth and missing female births using a Bayesian hierarchical time series mixture model (2021)
  7. Eggleston, B. S., Ibrahim, J. G., McNeil, B., Catellier, D: BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data (2021) not zbMATH
  8. Erler, N. S., Rizopoulos, D., Lesaffre, E. M. E. H.: JointAI: Joint Analysis and Imputation of Incomplete Data in R (2021) not zbMATH
  9. Nieto-Barajas, Luis E.; Targino, Rodrigo S.: A gamma moving average process for modelling dependence across development years in run-off triangles (2021)
  10. Perry de Valpine, Sally Paganin, Daniel Turek: compareMCMCs: An R package for studying MCMC efficiency (2021) not zbMATH
  11. Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
  12. Umlauf, N., Klein, N., Simon, T., Zeileis, A: bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond) (2021) not zbMATH
  13. Anne Philippe, Marie-Anne Vibet: Analysis of Archaeological Phases Using the R Package ArchaeoPhases (2020) not zbMATH
  14. David, Olivier; van Frank, Gaëlle; Goldringer, Isabelle; Rivière, Pierre; Turbet Delof, Michel: Bayesian inference of natural selection from spatiotemporal phenotypic data (2020)
  15. Hashimoto, E. M.; Ortega, E. M. M.; Cordeiro, G. M.; Suzuki, A. K.; Kattan, M. W.: The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis (2020)
  16. Oravecz, Zita; Vandekerckhove, Joachim: A joint process model of consensus and longitudinal dynamics (2020)
  17. Osthus, Dave; Hyman, Jeffrey D.; Karra, Satish; Panda, Nishant; Srinivasan, Gowri: A probabilistic clustering approach for identifying primary subnetworks of discrete fracture networks with quantified uncertainty (2020)
  18. Amoros, Ruben; King, Ruth; Toyoda, Hidenori; Kumada, Takashi; Johnson, Philip J.; Bird, Thomas G.: A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma (2019)
  19. Arellano-Valle, Reinaldo B.; Contreras-Reyes, Javier E.; Quintero, Freddy O. López; Valdebenito, Abel: A skew-normal dynamic linear model and Bayesian forecasting (2019)
  20. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv

1 2 3 4 next