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 162 articles , 1 standard article )

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  1. Andrade, Daniel; Takeda, Akiko; Fukumizu, Kenji: Robust Bayesian model selection for variable clustering with the Gaussian graphical model (2020)
  2. 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
  3. 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)
  4. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  5. Jauch, Michael; Hoff, Peter D.; Dunson, David B.: Random orthogonal matrices and the Cayley transform (2020)
  6. Jeffrey Pullin, Lyle Gurrin, Damjan Vukcevic: Rater: An R Package for Fitting Statistical Models of Repeated Categorical Ratings (2020) arXiv
  7. Jouni Helske: Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R (2020) arXiv
  8. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  9. Li, Yicheng; Raftery, Adrian E.: Estimating and forecasting the smoking-attributable mortality fraction for both genders jointly in over 60 countries (2020)
  10. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  11. Moores, Matthew; Nicholls, Geoff; Pettitt, Anthony; Mengersen, Kerrie: Scalable Bayesian inference for the inverse temperature of a hidden Potts model (2020)
  12. Mulder, Kees; Klugkist, Irene; van Renswoude, Daan; Visser, Ingmar: Mixtures of peaked power Batschelet distributions for circular data with application to saccade directions (2020)
  13. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  14. Renato Valladares Panaro: spsurv: An R package for semi-parametric survival analysis (2020) arXiv
  15. Riko Kelter: fbst: An R package for the Full Bayesian Significance Test for testing a sharp null hypothesis against its alternative via the e-value (2020) arXiv
  16. Robert J. B. Goudie, Rebecca M. Turner, Daniela De Angelis, Andrew Thomas: MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference (2020) not zbMATH
  17. Storlie, Curtis B.; Therneau, Terry M.; Carter, Rickey E.; Chia, Nicholas; Bergquist, John R.; Huddleston, Jeanne M.; Romero-Brufau, Santiago: Prediction and inference with missing data in patient alert systems (2020)
  18. Taysseer Sharaf; Theren Williams; Abdallah Chehade; Keshav Pokhrel: BLNN: An R package for training neural networks using Bayesian inference (2020) not zbMATH
  19. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  20. Vanslette, Kevin; Al Alsheikh, Abdullatif; Youcef-Toumi, Kamal: Why simple quadrature is just as good as Monte Carlo (2020)

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