To cite bbqr in publications use:

  Rubio Garcia F (2026). _bbqr: Bayesian Quantile Regression with Lasso
  and Adaptive Lasso_. R package version 0.1.0,
  <https://github.com/fernandorubiogarcia/bbqr>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {{bbqr}: Bayesian Quantile Regression with Lasso and Adaptive Lasso},
    author = {Fernando {Rubio Garcia}},
    year = {2026},
    note = {R package version 0.1.0},
    url = {https://github.com/fernandorubiogarcia/bbqr},
  }

For the binary adaptive-lasso method (bbqr_alasso) cite:

  Rubio Garcia F (2023). _Bayesian adaptive lasso binary quantile
  regression with hybrid resampling for classification of imbalanced
  data_. Master's thesis, Wichita State University, Dept. of
  Mathematics, Statistics, and Physics.
  <https://soar.wichita.edu/entities/publication/a2f86232-4704-4ec2-b685-751e7b04ec42>.

A BibTeX entry for LaTeX users is

  @MastersThesis{,
    title = {Bayesian adaptive lasso binary quantile regression with hybrid resampling for classification of imbalanced data},
    author = {Fernando {Rubio Garcia}},
    school = {Wichita State University, Dept. of Mathematics, Statistics, and Physics},
    year = {2023},
    url = {https://soar.wichita.edu/entities/publication/a2f86232-4704-4ec2-b685-751e7b04ec42},
  }

The continuous adaptive lasso (cbqr_alasso) at its default q = 1
reproduces the penalty of:

  Alhamzawi R, Yu K, Benoit D (2012). “Bayesian adaptive Lasso quantile
  regression.” _Statistical Modelling_, *12*(3), 279-297.
  doi:10.1177/1471082X1101200304
  <https://doi.org/10.1177/1471082X1101200304>.

A BibTeX entry for LaTeX users is

  @Article{,
    title = {Bayesian adaptive Lasso quantile regression},
    author = {Rahim Alhamzawi and Keming Yu and Dries F. Benoit},
    journal = {Statistical Modelling},
    year = {2012},
    volume = {12},
    number = {3},
    pages = {279--297},
    doi = {10.1177/1471082X1101200304},
  }

The lasso and unpenalised samplers implement published methods. Please
also cite the paper for the layer you used. Binary: Benoit and Van den
Poel (2012, doi:10.1002/jae.1216) for penalty = "none"; Benoit,
Al-Hamzawi and Yu (2013, doi:10.1007/s00180-013-0439-0) for penalty =
"lasso". Continuous: Kozumi and Kobayashi (2011,
doi:10.1080/00949655.2010.496117) for cbqr_none(), whose kernel is
their sampler; Benoit, Al-Hamzawi and Yu (2013) for cbqr_lasso(), whose
hierarchy is theirs carried over to an observed response.