Williams DR, Mulder J (2019). “BGGM: Bayesian Gaussian Graphical Models in R.” PsyArXiv. R package version 1.0.0, https://psyarxiv.com/t2cn7/.

Williams DR (2018). “Bayesian estimation for Gaussian graphical models: Structure learning, predictability, and network comparisons.” PsyArXiv. doi: 10.31234/osf.io/x8dpr, https://psyarxiv.com/x8dpr/.

Williams DR, Mulder J (2019). “Bayesian hypothesis testing for Gaussian graphical models: Conditional independence and order constraints.” PsyArXiv. doi: 10.31234/osf.io/ypxd8, https://psyarxiv.com/ypxd8/.

Williams DR, Philipe R, Luis PR, Mulder J (2020). “Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection.” Psychological Methods. doi: 10.1037/met0000254, https://doi.org/10.1037/met0000254.

Corresponding BibTeX entries:

  @Article{,
    title = {BGGM: Bayesian Gaussian Graphical Models in R},
    author = {Donald R. Williams and Joris Mulder},
    year = {2019},
    journal = {PsyArXiv},
    note = {R package version 1.0.0},
    url = {https://psyarxiv.com/t2cn7/},
  }

  @Article{,
    title = {Bayesian estimation for Gaussian graphical models:
      Structure learning, predictability, and network comparisons},
    author = {Donald R. Williams},
    year = {2018},
    journal = {PsyArXiv},
    url = {https://psyarxiv.com/x8dpr/},
    doi = {10.31234/osf.io/x8dpr},
  }

  @Article{,
    title = {Bayesian hypothesis testing for Gaussian graphical models:
      Conditional independence and order constraints},
    author = {Donald R. Williams and Joris Mulder},
    year = {2019},
    journal = {PsyArXiv},
    url = {https://psyarxiv.com/ypxd8/},
    doi = {10.31234/osf.io/ypxd8},
  }

  @Article{,
    title = {Comparing Gaussian graphical models with the posterior
      predictive distribution and Bayesian model selection.},
    author = {Donald R. Williams and Rast Philipe and Pericchi R. Luis
      and Joris Mulder},
    year = {2020},
    journal = {Psychological Methods},
    url = {https://doi.org/10.1037/met0000254},
    doi = {10.1037/met0000254},
  }