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https://cran.r-project.org/package=BDgraph

BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) &lt;<a href="https://doi.org/10.1214%2F14-BA889" target="_top">doi:10.1214/14-BA889</a>&gt;, Mohammadi et al. (2021) &lt;<a href="https://doi.org/10.1080%2F01621459.2021.1996377" target="_top">doi:10.1080/01621459.2021.1996377</a>&gt;, Dobra and Mohammadi (2018) &lt;<a href="https://doi.org/10.1214%2F18-AOAS1164" target="_top">doi:10.1214/18-AOAS1164</a>&gt;, and Mohammadi et al. (2023) &lt;<a href="https://doi.org/10.48550%2FarXiv.2307.00127" target="_top">doi:10.48550/arXiv.2307.00127</a>&gt;.



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BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

https://cran.r-project.org/package=BDgraph

Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) &lt;<a href="https://doi.org/10.1214%2F14-BA889" target="_top">doi:10.1214/14-BA889</a>&gt;, Mohammadi et al. (2021) &lt;<a href="https://doi.org/10.1080%2F01621459.2021.1996377" target="_top">doi:10.1080/01621459.2021.1996377</a>&gt;, Dobra and Mohammadi (2018) &lt;<a href="https://doi.org/10.1214%2F18-AOAS1164" target="_top">doi:10.1214/18-AOAS1164</a>&gt;, and Mohammadi et al. (2023) &lt;<a href="https://doi.org/10.48550%2FarXiv.2307.00127" target="_top">doi:10.48550/arXiv.2307.00127</a>&gt;.



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https://cran.r-project.org/package=BDgraph

BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) &lt;<a href="https://doi.org/10.1214%2F14-BA889" target="_top">doi:10.1214/14-BA889</a>&gt;, Mohammadi et al. (2021) &lt;<a href="https://doi.org/10.1080%2F01621459.2021.1996377" target="_top">doi:10.1080/01621459.2021.1996377</a>&gt;, Dobra and Mohammadi (2018) &lt;<a href="https://doi.org/10.1214%2F18-AOAS1164" target="_top">doi:10.1214/18-AOAS1164</a>&gt;, and Mohammadi et al. (2023) &lt;<a href="https://doi.org/10.48550%2FarXiv.2307.00127" target="_top">doi:10.48550/arXiv.2307.00127</a>&gt;.

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      Bayesian Structure Learning in Graphical Models using Birth-Death MCMC [R package BDgraph version 2.73]
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      Reza Mohammadi

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      BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC
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      Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) &lt;<a href="https://doi.org/10.1214%2F14-BA889" target="_top">doi:10.1214/14-BA889</a>&gt;, Mohammadi et al. (2021) &lt;<a href="https://doi.org/10.1080%2F01621459.2021.1996377" target="_top">doi:10.1080/01621459.2021.1996377</a>&gt;, Dobra and Mohammadi (2018) &lt;<a href="https://doi.org/10.1214%2F18-AOAS1164" target="_top">doi:10.1214/18-AOAS1164</a>&gt;, and Mohammadi et al. (2023) &lt;<a href="https://doi.org/10.48550%2FarXiv.2307.00127" target="_top">doi:10.48550/arXiv.2307.00127</a>&gt;.
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