RStan

RStan: the R interface to Stan. rstan: User-facing R functions are provided by this package 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 57 articles )

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  1. David Issa Mattos, Érika Martins Silva Ramos: Bayesian Paired-Comparison with the bpcs Package (2021) arXiv
  2. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  3. Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
  4. 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
  5. Anne Philippe, Marie-Anne Vibet: Analysis of Archaeological Phases Using the R Package ArchaeoPhases (2020) not zbMATH
  6. Gin, Brian; Sim, Nicholas; Skrondal, Anders; Rabe-Hesketh, Sophia: A dyadic IRT model (2020)
  7. Haaf, Julia M.; Merkle, Edgar C.; Rouder, Jeffrey N.: Do items order? The psychology in IRT models (2020)
  8. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  9. Jeffrey Pullin, Lyle Gurrin, Damjan Vukcevic: Rater: An R Package for Fitting Statistical Models of Repeated Categorical Ratings (2020) arXiv
  10. Jouni Helske: Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R (2020) arXiv
  11. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  12. Manevski, Damjan; Ružić Gorenjec, Nina; Kejžar, Nataša; Blagus, Rok: Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data (2020)
  13. Nolan, Tui H.; Menictas, Marianne; Wand, Matt P.: Streamlined variational inference with higher level random effects (2020)
  14. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  15. Renato Valladares Panaro: spsurv: An R package for semi-parametric survival analysis (2020) arXiv
  16. Rockwood, Nicholas J.: Maximum likelihood estimation of multilevel structural equation models with random slopes for latent covariates (2020)
  17. Taysseer Sharaf; Theren Williams; Abdallah Chehade; Keshav Pokhrel: BLNN: An R package for training neural networks using Bayesian inference (2020) not zbMATH
  18. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  19. van den Bergh, Don; Bogaerts, Stefan; Spreen, Marinus; Flohr, Rob; Vandekerckhove, Joachim; Batchelder, William H.; Wagenmakers, Eric-Jan: Cultural consensus theory for the evaluation of patients’ mental health scores in forensic psychiatric hospitals (2020)
  20. Yan, Hongxuan; Peters, Gareth W.; Chan, Jennifer S. K.: Multivariate long-memory cohort mortality models (2020)

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