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

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References in zbMATH (referenced in 50 articles )

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  1. Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
  2. Anne Philippe, Marie-Anne Vibet: Analysis of Archaeological Phases Using the R Package ArchaeoPhases (2020) not zbMATH
  3. David, Olivier; van Frank, Gaëlle; Goldringer, Isabelle; Rivière, Pierre; Turbet Delof, Michel: Bayesian inference of natural selection from spatiotemporal phenotypic data (2020)
  4. Oravecz, Zita; Vandekerckhove, Joachim: A joint process model of consensus and longitudinal dynamics (2020)
  5. 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)
  6. 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)
  7. Arellano-Valle, Reinaldo B.; Contreras-Reyes, Javier E.; Quintero, Freddy O. López; Valdebenito, Abel: A skew-normal dynamic linear model and Bayesian forecasting (2019)
  8. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  9. Nemmers, Thomas; Narayan, Anjana; Banerjee, Sudipto: Bayesian modeling and uncertainty quantification for descriptive social networks (2019)
  10. Osthus, Dave; Gattiker, James; Priedhorsky, Reid; Del Valle, Sara Y.: Dynamic Bayesian influenza forecasting in the United States with hierarchical discrepancy (with discussion) (2019)
  11. Pohl, Steffi; Ulitzsch, Esther; von Davier, Matthias: Using response times to model not-reached items due to time limits (2019)
  12. Bauer, Alexander; Scheipl, Fabian; Küchenhoff, Helmut; Gabriel, Alice-Agnes: An introduction to semiparametric function-on-scalar regression (2018)
  13. Draxler, Clemens: Bayesian conditional inference for Rasch models (2018)
  14. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
  15. Gómez-Rubio, Virgilio; Rue, Håvard: Markov chain Monte Carlo with the integrated nested Laplace approximation (2018)
  16. Kopra, Juho; Karvanen, Juha; Härkänen, Tommi: Bayesian models for data missing not at random in health examination surveys (2018)
  17. Lock, Eric F.; Kohli, Nidhi; Bose, Maitreyee: Detecting multiple random changepoints in Bayesian piecewise growth mixture models (2018)
  18. Chen Dong; Michel Wedel: BANOVA: An R Package for Hierarchical Bayesian ANOVA (2017) not zbMATH
  19. Greven, Sonja; Scheipl, Fabian: A general framework for functional regression modelling (2017)
  20. Lifeng Lin; Jing Zhang; James Hodges; Haitao Chu: Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package (2017) not zbMATH

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