rstan

R package rstan. User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the ’StanHeaders’ package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.


References in zbMATH (referenced in 55 articles )

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  1. Edgar Santos-Fernandez, Jay M. Ver Hoef, James M. McGree, Daniel J. Isaak, Kerrie Mengersen, Erin E. Peterson: SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks (2022) arXiv
  2. Etienne Côme, Nicolas Jouvin : greed: An R Package for Model-Based Clustering by Greedy Maximization of the Integrated Classification Likelihood (2022) arXiv
  3. Fisher, Thomas J.; Zhang, Jing; Colegate, Stephen P.; Vanni, Michael J.: Detecting and modeling changes in a time series of proportions (2022)
  4. Papastamoulis, Panagiotis; Ntzoufras, Ioannis: On the identifiability of Bayesian factor analytic models (2022)
  5. Pieschner, Susanne; Hasenauer, Jan; Fuchs, Christiane: Identifiability analysis for models of the translation kinetics after mRNA transfection (2022)
  6. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
  7. Zhou, Haiming; Huang, Xianzheng: Bayesian beta regression for bounded responses with unknown supports (2022)
  8. Asar, Özgür: Bayesian analysis of Turkish income and living conditions data, using clustered longitudinal ordinal modelling with bridge distributed random effects (2021)
  9. Cordoba, Karen Rosana; Montenegro, Alvaro Mauricio: Bayesian multi-faceted TRI models for measuring professor’s performance in the classroom (2021)
  10. Dai, Chenguang; Chan, Duo; Huybers, Peter; Pillai, Natesh: Late 19th century navigational uncertainties and their influence on sea surface temperature estimates (2021)
  11. Gronau, Q. F., Raj K. N., A., Wagenmakers, E.-J.: Informed Bayesian Inference for the A/B Test (2021) not zbMATH
  12. James A. Scott, Axel Gandy, Swapnil Mishra, Samir Bhatt, Seth Flaxman, H. Juliette T. Unwin, Jonathan Ish-Horowicz: Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes (2021) arXiv
  13. Li, Yicheng; Raftery, Adrian E.: Accounting for smoking in forecasting mortality and life expectancy (2021)
  14. Menictas, M.; Nolan, T. H.; Simpson, D. G.; Wand, M. P.: Streamlined variational inference for higher level group-specific curve models (2021)
  15. Merkle, E. C., Fitzsimmons, E., Uanhoro, J., Goodrich, B. : Efficient Bayesian Structural Equation Modeling in Stan (2021) not zbMATH
  16. Paul-Christian Burkner: Bayesian Item Response Modeling in R with brms and Stan (2021) not zbMATH
  17. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  18. Robertson, Nathan; Flegal, James M.; Vats, Dootika; Jones, Galin L.: Assessing and visualizing simultaneous simulation error (2021)
  19. Schramm, Pele; Batchelder, William H.: Hierarchical paired comparison modeling, a cultural consensus theory approach (2021)
  20. Shan, Mingyang; Thomas, Kali S.; Gutman, Roee: A multiple imputation procedure for record linkage and causal inference to estimate the effects of home-delivered meals (2021)

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