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Williams DR, Mulder J (2019).“BGGM: Bayesian Gaussian Graphical Models in R.”PsyArXiv.R package version 2.1.6,https://osf.io/preprints/psyarxiv/t2cn7.

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

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

Williams DR, Philipe R, Luis PR, Mulder J (2020).“Comparing Gaussian graphical models with the posterior predictivedistribution 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 2.1.6},    url = {https://osf.io/preprints/psyarxiv/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://osf.io/preprints/psyarxiv/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://osf.io/preprints/psyarxiv/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},  }

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