RStan

RStan: the R interface to Stan. rstan: User-facing R functions are provided by this package 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 91 articles )

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  1. Aliverti, Emanuele; Russo, Massimiliano: Stratified stochastic variational inference for high-dimensional network factor model (2022)
  2. Botsas, Themistoklis; Mason, Lachlan R.; Pan, Indranil: Rule-based Bayesian regression (2022)
  3. De Stefano, Domenico; Pauli, Francesco; Torelli, Nicola: Preelectoral polls variability: a hierarchical Bayesian model to assess the role of house effects with application to Italian elections (2022)
  4. Fisher, Thomas J.; Zhang, Jing; Colegate, Stephen P.; Vanni, Michael J.: Detecting and modeling changes in a time series of proportions (2022)
  5. Frick, Susanne: Modeling faking in the multidimensional forced-choice format: the faking mixture model (2022)
  6. Janicki, Ryan; Raim, Andrew M.; Holan, Scott H.; Maples, Jerry J.: Bayesian nonparametric multivariate spatial mixture mixed effects models with application to American community survey special tabulations (2022)
  7. Papastamoulis, Panagiotis; Ntzoufras, Ioannis: On the identifiability of Bayesian factor analytic models (2022)
  8. Pieschner, Susanne; Hasenauer, Jan; Fuchs, Christiane: Identifiability analysis for models of the translation kinetics after mRNA transfection (2022)
  9. Tourani-Farani, Fahimeh; Kazemi, Iraj: Transformed mixed-effects modeling of correlated bounded and positive data with a novel multivariate generalized Johnson distribution (2022)
  10. Ulitzsch, Esther; Pohl, Steffi; Khorramdel, Lale; Kroehne, Ulf; von Davier, Matthias: A response-time-based latent response mixture model for identifying and modeling careless and insufficient effort responding in survey data (2022)
  11. Zhou, Haiming; Huang, Xianzheng: Bayesian beta regression for bounded responses with unknown supports (2022)
  12. Asar, Özgür: Bayesian analysis of Turkish income and living conditions data, using clustered longitudinal ordinal modelling with bridge distributed random effects (2021)
  13. Auerbach, Jonathan; Eshleman, Christopher; Trangucci, Rob: A hierarchical Bayes approach to adjust for selection bias in before-after analyses of vision zero policies (2021)
  14. Cordoba, Karen Rosana; Montenegro, Alvaro Mauricio: Bayesian multi-faceted TRI models for measuring professor’s performance in the classroom (2021)
  15. Dai, Chenguang; Chan, Duo; Huybers, Peter; Pillai, Natesh: Late 19th century navigational uncertainties and their influence on sea surface temperature estimates (2021)
  16. David Issa Mattos, Érika Martins Silva Ramos: Bayesian Paired-Comparison with the bpcs Package (2021) arXiv
  17. Francisco Palmí-Perales, Virgilio Gómez-Rubio, Miguel A. Martinez-Beneito: Bayesian Multivariate Spatial Models for Lattice Data with INLA (2021) not zbMATH
  18. Kelter, Riko: Bayesian model selection in the (\mathcalM)-open setting -- approximate posterior inference and subsampling for efficient large-scale leave-one-out cross-validation via the difference estimator (2021)
  19. Li, Yicheng; Raftery, Adrian E.: Accounting for smoking in forecasting mortality and life expectancy (2021)
  20. Mayrink, V. D., Duarte, J. D. N., Demarqui, F. N.: pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution (2021) not zbMATH

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