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 20 articles )

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  1. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  2. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  3. Yan, Hongxuan; Peters, Gareth W.; Chan, Jennifer S. K.: Multivariate long-memory cohort mortality models (2020)
  4. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  5. Fu, Zhihui; Wu, Jian; Ma, Mingyue: Application of Rstan package in parameter estimation of four-parameter logistic model (2019)
  6. Gronau, Quentin F.; Wagenmakers, Eric-Jan; Heck, Daniel W.; Matzke, Dora: A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling (2019)
  7. Hystad, Grethe; Eleish, Ahmed; Hazen, Robert M.; Morrison, Shaunna M.; Downs, Robert T.: Bayesian estimation of Earth’s undiscovered mineralogical diversity using noninformative priors (2019)
  8. Lewis-Beck, Colin; Zhu, Zhengyuan; Mondal, Anirban; Song, Joon Jin; Hobbs, Jonathan; Hornbuckle, Brian; Patton, Jason: A parametric approach to unmixing remote sensing crop growth signatures (2019)
  9. Ntzoufras, Ioannis; Tarantola, Claudia; Lupparelli, Monia: Probability based independence sampler for Bayesian quantitative learning in graphical log-linear marginal models (2019)
  10. Quijano Xacur, Oscar Alberto: The unifed distribution (2019)
  11. Rodrigues, T.; Dortet-Bernadet, J.-L.; Fan, Y.: Simultaneous Fitting of Bayesian penalised quantile splines (2019)
  12. Tanaka, Emi; Hui, Francis K. C.: Symbolic formulae for linear mixed models (2019)
  13. van Erp, Sara; Oberski, Daniel L.; Mulder, Joris: Shrinkage priors for Bayesian penalized regression (2019)
  14. Zhang, Lu; Datta, Abhirup; Banerjee, Sudipto: Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments (2019)
  15. Craig Wang; Reinhard Furrer: eggCounts: a Bayesian hierarchical toolkit to model faecal egg count reductions (2018) arXiv
  16. Pagendam, Dan; Snoad, Nigel; Yang, Wen-Hsi; Segoli, Michal; Ritchie, Scott; Trewin, Brendan; Beebe, Nigel: Improving estimates of Fried’s index from mating competitiveness experiments (2018)
  17. Quijano Xacur, Oscar Alberto; Garrido, José: Bayesian credibility for GLMs (2018)
  18. Ross, Cody; Pacheco-Cobos, Luis; Winterhalder, Bruce: A general model of forager search: adaptive encounter-conditional heuristics outperform Lévy flights in the search for patchily distributed prey (2018)
  19. Gronau, Quentin F.; Sarafoglou, Alexandra; Matzke, Dora; Ly, Alexander; Boehm, Udo; Marsman, Maarten; Leslie, David S.; Forster, Jonathan J.; Wagenmakers, Eric-Jan; Steingroever, Helen: A tutorial on bridge sampling (2017)
  20. Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers: bridgesampling: An R Package for Estimating Normalizing Constants (2017) arXiv