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 174 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. 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
  3. 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
  4. 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)
  5. Fisher, Christopher R.; Houpt, Joseph W.; Gunzelmann, Glenn: Developing memory-based models of ACT-R within a statistical framework (2020)
  6. Fouskakis, D.; Petrakos, G.; Rotous, I.: A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators (2020)
  7. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  8. Jacob Leander, Joachim Almquist, Anna Johnning, Julia Larsson, Mats Jirstrand: NLMEModeling: A Wolfram Mathematica Package for Nonlinear Mixed Effects Modeling of Dynamical Systems (2020) arXiv
  9. Jauch, Michael; Hoff, Peter D.; Dunson, David B.: Random orthogonal matrices and the Cayley transform (2020)
  10. Jeffrey Pullin, Lyle Gurrin, Damjan Vukcevic: Rater: An R Package for Fitting Statistical Models of Repeated Categorical Ratings (2020) arXiv
  11. Jouni Helske: Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R (2020) arXiv
  12. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  13. Lijoi, Antonio; Prünster, Igor; Rigon, Tommaso: Sampling hierarchies of discrete random structures (2020)
  14. Li, Yicheng; Raftery, Adrian E.: Estimating and forecasting the smoking-attributable mortality fraction for both genders jointly in over 60 countries (2020)
  15. Ma, Zhihua; Chen, Guanghui: Bayesian semiparametric latent variable model with DP prior for joint analysis: implementation with nimble (2020)
  16. Michalkiewicz, Martha; Horn, Sebastian S.; Bayen, Ute J.: Hierarchical multinomial modeling to explain individual differences in children’s clustering in free recall (2020)
  17. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  18. Moores, Matthew; Nicholls, Geoff; Pettitt, Anthony; Mengersen, Kerrie: Scalable Bayesian inference for the inverse temperature of a hidden Potts model (2020)
  19. Mulder, Kees; Klugkist, Irene; van Renswoude, Daan; Visser, Ingmar: Mixtures of peaked power Batschelet distributions for circular data with application to saccade directions (2020)
  20. Oliver Schulz, Frederik Beaujean, Allen Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kröninger, Salvatore La Cagnina, Lars Röhrig, Lolian Shtembari: BAT.jl - A Julia-based tool for Bayesian inference (2020) arXiv

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