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 39 articles )

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  1. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  2. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  3. Renato Valladares Panaro: spsurv: An R package for semi-parametric survival analysis (2020) arXiv
  4. Taysseer Sharaf; Theren Williams; Abdallah Chehade; Keshav Pokhrel: BLNN: An R package for training neural networks using Bayesian inference (2020) not zbMATH
  5. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  6. Yan, Hongxuan; Peters, Gareth W.; Chan, Jennifer S. K.: Multivariate long-memory cohort mortality models (2020)
  7. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  8. Antonio Calcagnì, Massimiliano Pastore, Gianmarco Altoè: ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in R (2019) arXiv
  9. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  10. George G Vega Yon; Paul Marjoram: fmcmc: A friendly MCMC framework (2019) not zbMATH
  11. 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)
  12. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  13. 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)
  14. 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)
  15. Ntzoufras, Ioannis; Tarantola, Claudia; Lupparelli, Monia: Probability based independence sampler for Bayesian quantitative learning in graphical log-linear marginal models (2019)
  16. Quijano Xacur, Oscar Alberto: The unifed distribution (2019)
  17. Rodrigues, T.; Dortet-Bernadet, J.-L.; Fan, Y.: Simultaneous Fitting of Bayesian penalised quantile splines (2019)
  18. Shana Scogin; Johannes Karreth; Andreas Beger; Rob Williams: BayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimation (2019) not zbMATH
  19. van Erp, Sara; Oberski, Daniel L.; Mulder, Joris: Shrinkage priors for Bayesian penalized regression (2019)
  20. Veen, Duco; Klugkist, Irene: Standard errors, priors, and bridge sampling: a discussion of Liu et al. (2019)

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