R package brms. brms: Bayesian Regression Models using Stan. Fit Bayesian generalized (non-)linear multilevel models using Stan for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit – among others – linear, robust linear, binomial, Poisson, survival, response times, ordinal, zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling options include auto-correlation and smoothing terms, user defined dependence structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.

References in zbMATH (referenced in 32 articles , 1 standard article )

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  1. Bonner, S., Kim, H.-N., Westneat, D., Mutzel, A., Wright, J., Schofield, M.: dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble (2021) not zbMATH
  2. Bürkner, Paul-Christian; Gabry, Jonah; Vehtari, Aki: Efficient leave-one-out cross-validation for Bayesian non-factorized normal and student-(t) models (2021)
  3. Gronau, Q. F., Raj K. N., A., Wagenmakers, E.-J.: Informed Bayesian Inference for the A/B Test (2021) not zbMATH
  4. Kuschnig, N., Vashold, L.: BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R (2021) not zbMATH
  5. Merkle, E. C., Fitzsimmons, E., Uanhoro, J., Goodrich, B. : Efficient Bayesian Structural Equation Modeling in Stan (2021) not zbMATH
  6. Paul-Christian Burkner: Bayesian Item Response Modeling in R with brms and Stan (2021) not zbMATH
  7. Ryan Hornby, Jingchen Hu: Bayesian Estimation of Attribute Disclosure Risks in Synthetic Data with the AttributeRiskCalculation R Package (2021) arXiv
  8. Umlauf, N., Klein, N., Simon, T., Zeileis, A: bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond) (2021) not zbMATH
  9. Watson, Robin; Morgan, Thomas J. H.; Kendal, Rachel L.; Van de Vyver, Julie; Kendal, Jeremy: Social learning strategies and cooperative behaviour: evidence of payoff bias, but not prestige or conformity, in a social dilemma game (2021)
  10. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  11. Aaron Cochrane: TEfits: Nonlinear regression for time-evolving indices (2020) not zbMATH
  12. Alkhairy, Ibrahim; Low-Choy, Samantha; Murray, Justine; Wang, Junhu; Pettitt, Anthony: Quantifying conditional probability tables in Bayesian networks: Bayesian regression for scenario-based encoding of elicited expert assessments on feral pig habitat (2020)
  13. Bürkner, Paul-Christian; Gabry, Jonah; Vehtari, Aki: Approximate leave-future-out cross-validation for Bayesian time series models (2020)
  14. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  15. Kristensen, Simon Bang; Sandberg, Kristian; Bibby, Bo Martin: Regression methods for metacognitive sensitivity (2020)
  16. McManus, Scott; Rahman, Azizur; Horta, Ana; Coombes, Jacqueline: Applied Bayesian modeling for assessment of interpretation uncertainty in spatial domains (2020)
  17. Piironen, Juho; Paasiniemi, Markus; Vehtari, Aki: Projective inference in high-dimensional problems: prediction and feature selection (2020)
  18. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  19. Tomás Capretto, Camen Piho, Ravin Kumar, Jacob Westfall, Tal Yarkoni, Osvaldo A. Martin: Bambi: A simple interface for fitting Bayesian linear models in Python (2020) arXiv
  20. Dominique Makowski, Mattan S. Ben-Shachar, Daniel Lüdecke: bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework (2019) not zbMATH

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