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.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
- Rodrigues, T.; Dortet-Bernadet, J.-L.; Fan, Y.: Simultaneous Fitting of Bayesian penalised quantile splines (2019)
- Craig Wang; Reinhard Furrer: eggCounts: a Bayesian hierarchical toolkit to model faecal egg count reductions (2018) arXiv
- Pagendam, Dan; Snoad, Nigel; Yang, Wen-Hsi; Segoli, Michal; Ritchie, Scott; Trewin, Brendan; Beebe, Nigel: Improving estimates of Fried’s index from mating competitiveness experiments (2018)
- Quijano Xacur, Oscar Alberto; Garrido, José: Bayesian credibility for GLMs (2018)
- Ross, Cody; Pacheco-Cobos, Luis; Winterhalder, Bruce: A general model of forager search: adaptive encounter-conditional heuristics outperform Lévy flights in the search for patchily distributed prey (2018)
- Gronau, Quentin F.; Sarafoglou, Alexandra; Matzke, Dora; Ly, Alexander; Boehm, Udo; Marsman, Maarten; Leslie, David S.; Forster, Jonathan J.; Wagenmakers, Eric-Jan; Steingroever, Helen: A tutorial on bridge sampling (2017)
- Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers: bridgesampling: An R Package for Estimating Normalizing Constants (2017) arXiv