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brms: An R Package for Bayesian Multilevel Models Using Stan

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  • Bürkner, Paul-Christian

Abstract

The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan. A wide range of distributions and link functions are supported, allowing users to fit - among others - linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling options include autocorrelation of the response variable, user defined covariance structures, censored data, as well as meta-analytic standard errors. 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 the Watanabe-Akaike information criterion and leave-one-out cross-validation.

Suggested Citation

  • Bürkner, Paul-Christian, 2017. "brms: An R Package for Bayesian Multilevel Models Using Stan," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i01).
    • Handle: RePEc:jss:jstsof:v:080:i01
    DOI: http://hdl.handle.net/10.18637/jss.v080.i01
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    References listed on IDEAS

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    1. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    2. Yee, Thomas W., 2010. "The VGAM Package for Categorical Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i10).
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    2. Eline Adrianne Smit & Andrew J Milne & Roger T Dean & Gabrielle Weidemann, 2019. "Perception of affect in unfamiliar musical chords," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-28, June.
    3. Strauss, Ilan & Yang, Jangho, 2020. "Corporate Secular Stagnation: Empirical Evidence on the Advanced Economy Investment Slowdown," INET Oxford Working Papers 2019-16, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    4. Kubinec, Robert & Barceló, Joan & Goldszmidt, Rafael & Grujic, Vanja & Model, Timothy & Schenk, Caress & Cheng, Cindy & Hale, Thomas & Hartnett, Allison Spencer & Messerschmidt, Luca, 2021. "Statistically Validated Indices for COVID-19 Public Health Policies," SocArXiv rn9xk, Center for Open Science.
    5. Miranda Dally & Jaime Butler-Dawson & Alex Cruz & Lyndsay Krisher & Richard J Johnson & Claudia Asensio & W Daniel Pilloni & Edwin J Asturias & Lee S Newman, 2020. "Longitudinal trends in renal function among first time sugarcane harvesters in Guatemala," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-11, March.
    6. Elsner, James B. & Schroder, Zoe, 2019. "Tornado damage ratings estimated with cumulative logistic regression," Earth Arxiv k9wv6, Center for Open Science.
    7. Lieke L F van Lieshout & Iris J Traast & Floris P de Lange & Roshan Cools, 2021. "Curiosity or savouring? Information seeking is modulated by both uncertainty and valence," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-19, September.
    8. Maxim Ananyev, 2019. "Political Economy of Cross-Border Income Shifting: A Protection Racket Approach," Melbourne Institute Working Paper Series wp2019n15, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    9. Joshua P White & Simon Dennis & Martin Tomko & Jessica Bell & Stephan Winter, 2021. "Paths to social licence for tracking-data analytics in university research and services," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-19, May.

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