Stan

Stan: A C++ Library for Probability and Sampling. Stan is a probabilistic programming language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum likelihood estimation with Optimization (BFGS). Stan is coded in C++ and runs on all major platforms.


References in zbMATH (referenced in 192 articles , 1 standard article )

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  1. David Issa Mattos, Érika Martins Silva Ramos: Bayesian Paired-Comparison with the bpcs Package (2021) arXiv
  2. James Yang: FastAD: Expression Template-Based C++ Library for Fast and Memory-Efficient Automatic Differentiation (2021) arXiv
  3. John Taylor Chavis, Amy Louise Cochran, Christopher James Earls: CU-MSDSp: A flexible parallelized Reversible jump Markov chain Monte Carlo method (2021) not zbMATH
  4. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  5. Mauff, Katya; Erler, Nicole S.; Kardys, Isabella; Rizopoulos, Dimitris: Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model (2021)
  6. Nemeth, Christopher; Fearnhead, Paul: Stochastic gradient Markov chain Monte Carlo (2021)
  7. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  8. Rosner, Gary L.; Laud, Purushottam W.; Johnson, Wesley O.: Bayesian thinking in biostatistics (2021)
  9. William Michael Landau: The stantargets R package: a workflow framework for efficient reproducible Stan-powered Bayesian data analysis pipelines (2021) not zbMATH
  10. Andrade, Daniel; Takeda, Akiko; Fukumizu, Kenji: Robust Bayesian model selection for variable clustering with the Gaussian graphical model (2020)
  11. Angus McLure, Ben O’Neill, Helen Mayfield, Colleen Lau, Brady McPherson: PoolTestR: An R package for estimating prevalence and regression modelling with pooled samples (2020) arXiv
  12. Anne Philippe, Marie-Anne Vibet: Analysis of Archaeological Phases Using the R Package ArchaeoPhases (2020) not zbMATH
  13. Brehmer, Johann; Louppe, Gilles; Pavez, Juan; Cranmer, Kyle: Mining gold from implicit models to improve likelihood-free inference (2020)
  14. Calafat, Francisco M.; Marcos, Marta: Probabilistic reanalysis of storm surge extremes in Europe (2020)
  15. Chenguang Wang, Elizabeth Colantuoni, Andrew Leroux, Daniel O. Scharfstein: idem: An R Package for Inferences in Clinical Trials with Death and Missingness (2020) not zbMATH
  16. Cho, Sun-Joo; Brown-Schmidt, Sarah; De Boeck, Paul; Shen, Jianhong: Modeling intensive polytomous time-series eye-tracking data: a dynamic tree-based item response model (2020)
  17. Fisher, Christopher R.; Houpt, Joseph W.; Gunzelmann, Glenn: Developing memory-based models of ACT-R within a statistical framework (2020)
  18. Fouskakis, D.; Petrakos, G.; Rotous, I.: A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators (2020)
  19. Gin, Brian; Sim, Nicholas; Skrondal, Anders; Rabe-Hesketh, Sophia: A dyadic IRT model (2020)
  20. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv

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