rstan

R package rstan. User-facing R functions are provided 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 35 articles )

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  1. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
  2. Dai, Chenguang; Chan, Duo; Huybers, Peter; Pillai, Natesh: Late 19th century navigational uncertainties and their influence on sea surface temperature estimates (2021)
  3. Gronau, Q. F., Raj K. N., A., Wagenmakers, E.-J.: Informed Bayesian Inference for the A/B Test (2021) not zbMATH
  4. Li, Yicheng; Raftery, Adrian E.: Accounting for smoking in forecasting mortality and life expectancy (2021)
  5. Merkle, E. C., Fitzsimmons, E., Uanhoro, J., Goodrich, B. : Efficient Bayesian Structural Equation Modeling in Stan (2021) not zbMATH
  6. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  7. Schramm, Pele; Batchelder, William H.: Hierarchical paired comparison modeling, a cultural consensus theory approach (2021)
  8. Shan, Mingyang; Thomas, Kali S.; Gutman, Roee: A multiple imputation procedure for record linkage and causal inference to estimate the effects of home-delivered meals (2021)
  9. Gin, Brian; Sim, Nicholas; Skrondal, Anders; Rabe-Hesketh, Sophia: A dyadic IRT model (2020)
  10. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  11. Liu, Siyang; Cai, Yan: Using Stan to implement Bayesian parameter estimation of IRT 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. Nguyen, Hoang; Ausín, M. Concepción; Galeano, Pedro: Variational inference for high dimensional structured factor copulas (2020)
  14. Rockwood, Nicholas J.: Maximum likelihood estimation of multilevel structural equation models with random slopes for latent covariates (2020)
  15. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  16. 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)
  17. Yan, Hongxuan; Peters, Gareth W.; Chan, Jennifer S. K.: Multivariate long-memory cohort mortality models (2020)
  18. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  19. Fu, Zhihui; Wu, Jian; Ma, Mingyue: Application of Rstan package in parameter estimation of four-parameter logistic model (2019)
  20. Gronau, Quentin F.; Wagenmakers, Eric-Jan; Heck, Daniel W.; Matzke, Dora: A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling (2019)

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