bayesTLS fits its models with brms and Stan. To cite bayesTLS in publications, please cite the package (below) together with the brms and Stan (RStan) references it relies on:

Noble D, Arnold P, Nakagawa S, Pottier P (2026). “A flexible modelling framework for estimating thermal tolerance and sensitivity.” Manuscript in preparation, https://github.com/daniel1noble/bayesTLS.

Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models Using Stan.” Journal of Statistical Software, 80(1), 1–28. doi:10.18637/jss.v080.i01.

Stan Development Team (2024). RStan: the R Interface to Stan. R package, https://mc-stan.org/.

The default sampling backend is cmdstanr; if you fit with cmdstanr rather than RStan, please cite it instead (run citation("cmdstanr")).

Corresponding BibTeX entries:

  @Unpublished{,
    title = {A flexible modelling framework for estimating thermal
      tolerance and sensitivity},
    author = {Daniel W. A. Noble and Pieter A. Arnold and Shinichi
      Nakagawa and Patrice Pottier},
    year = {2026},
    note = {Manuscript in preparation},
    url = {https://github.com/daniel1noble/bayesTLS},
  }

  @Article{,
    title = {{brms}: An {R} Package for {Bayesian} Multilevel Models
      Using {Stan}},
    author = {Paul-Christian Bürkner},
    journal = {Journal of Statistical Software},
    year = {2017},
    volume = {80},
    number = {1},
    pages = {1--28},
    doi = {10.18637/jss.v080.i01},
  }

  @Manual{,
    title = {{RStan}: the {R} Interface to {Stan}},
    author = {{Stan Development Team}},
    year = {2024},
    note = {R package},
    url = {https://mc-stan.org/},
  }