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 199 articles , 1 standard article )

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  1. Brandon P.M. Edwards, Adam C. Smith: bbsBayes: An R Package for Hierarchical Bayesian Analysis of North American Breeding Bird Survey Data (2021) not zbMATH
  2. Bürkner, Paul-Christian; Gabry, Jonah; Vehtari, Aki: Efficient leave-one-out cross-validation for Bayesian non-factorized normal and student-(t) models (2021)
  3. David Issa Mattos, Érika Martins Silva Ramos: Bayesian Paired-Comparison with the bpcs Package (2021) arXiv
  4. Ibargüen-Mondragón, Eduardo; Prieto, Kernel; Hidalgo-Bonilla, Sandra Patricia: A model on bacterial resistance considering a generalized law of mass action for plasmid replication (2021)
  5. James Yang: FastAD: Expression Template-Based C++ Library for Fast and Memory-Efficient Automatic Differentiation (2021) arXiv
  6. John Taylor Chavis, Amy Louise Cochran, Christopher James Earls: CU-MSDSp: A flexible parallelized Reversible jump Markov chain Monte Carlo method (2021) not zbMATH
  7. Kelter, Riko: Analysis of type I and II error rates of Bayesian and frequentist parametric and nonparametric two-sample hypothesis tests under preliminary assessment of normality (2021)
  8. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  9. 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)
  10. Nemeth, Christopher; Fearnhead, Paul: Stochastic gradient Markov chain Monte Carlo (2021)
  11. Philippe Rast; Stephen Martin: bmgarch: An R-Package for Bayesian Multivariate GARCH models (2021) not zbMATH
  12. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  13. Rosner, Gary L.; Laud, Purushottam W.; Johnson, Wesley O.: Bayesian thinking in biostatistics (2021)
  14. William Michael Landau: The stantargets R package: a workflow framework for efficient reproducible Stan-powered Bayesian data analysis pipelines (2021) not zbMATH
  15. Andrade, Daniel; Takeda, Akiko; Fukumizu, Kenji: Robust Bayesian model selection for variable clustering with the Gaussian graphical model (2020)
  16. 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
  17. Anne Philippe, Marie-Anne Vibet: Analysis of Archaeological Phases Using the R Package ArchaeoPhases (2020) not zbMATH
  18. Brehmer, Johann; Louppe, Gilles; Pavez, Juan; Cranmer, Kyle: Mining gold from implicit models to improve likelihood-free inference (2020)
  19. Calafat, Francisco M.; Marcos, Marta: Probabilistic reanalysis of storm surge extremes in Europe (2020)
  20. 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

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