
Authors and Citation
Authors
-
Donald Williams. Author.
-
Joris Mulder. Author.
-
Philippe Rast. Author, maintainer.
Citation
Source: inst/CITATION
Williams DR, Mulder J (2019). “BGGM: Bayesian Gaussian Graphical Models in R.” PsyArXiv. R package version 2.1.4, https://osf.io/preprints/psyarxiv/t2cn7.
@Article{,
title = {BGGM: Bayesian Gaussian Graphical Models in R},
author = {Donald R. Williams and Joris Mulder},
year = {2019},
journal = {PsyArXiv},
note = {R package version 2.1.4},
url = {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/.
@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},
}
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://osf.io/preprints/psyarxiv/ypxd8/.
@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},
}
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.
@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},
}