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 28 articles )

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  1. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  2. Gin, Brian; Sim, Nicholas; Skrondal, Anders; Rabe-Hesketh, Sophia: A dyadic IRT model (2020)
  3. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  4. Liu, Siyang; Cai, Yan: Using Stan to implement Bayesian parameter estimation of IRT models (2020)
  5. Manevski, Damjan; Ružić Gorenjec, Nina; Kejžar, Nataša; Blagus, Rok: Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data (2020)
  6. Nguyen, Hoang; Ausín, M. Concepción; Galeano, Pedro: Variational inference for high dimensional structured factor copulas (2020)
  7. Rockwood, Nicholas J.: Maximum likelihood estimation of multilevel structural equation models with random slopes for latent covariates (2020)
  8. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  9. 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)
  10. Yan, Hongxuan; Peters, Gareth W.; Chan, Jennifer S. K.: Multivariate long-memory cohort mortality models (2020)
  11. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  12. Fu, Zhihui; Wu, Jian; Ma, Mingyue: Application of Rstan package in parameter estimation of four-parameter logistic model (2019)
  13. 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)
  14. Hystad, Grethe; Eleish, Ahmed; Hazen, Robert M.; Morrison, Shaunna M.; Downs, Robert T.: Bayesian estimation of Earth’s undiscovered mineralogical diversity using noninformative priors (2019)
  15. Lewis-Beck, Colin; Zhu, Zhengyuan; Mondal, Anirban; Song, Joon Jin; Hobbs, Jonathan; Hornbuckle, Brian; Patton, Jason: A parametric approach to unmixing remote sensing crop growth signatures (2019)
  16. Ntzoufras, Ioannis; Tarantola, Claudia; Lupparelli, Monia: Probability based independence sampler for Bayesian quantitative learning in graphical log-linear marginal models (2019)
  17. Quijano Xacur, Oscar Alberto: The unifed distribution (2019)
  18. Rodrigues, T.; Dortet-Bernadet, J.-L.; Fan, Y.: Simultaneous Fitting of Bayesian penalised quantile splines (2019)
  19. Schmertmann, Carl P.; Hauer, Mathew E.: Bayesian estimation of total fertility from a population’s age-sex structure (2019)
  20. Tanaka, Emi; Hui, Francis K. C.: Symbolic formulae for linear mixed models (2019)

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