Bayesian Structure Learning in Graphical Models using Birth-Death MCMC
Bayesian estimation of (zero-inflated) Discrete Weibull regression
Extract links from an adjacency matrix
Compute the area under the ROC curve
Bayesian Structure Learning in Graphical Models
Search algorithm for Gaussian copula graphical models for count data
Search algorithm in graphical models using marginal pseudo-likehlihood
Nonparametric transfer
Search algorithm in graphical models
Graph data simulation
Bayes factor for two graphs
Graph structure comparison
Plot Confusion Matrix
Confusion Matrix
Estimated covariance matrix
The Discrete Weibull Distribution (Type 1)
Normalizing constant for G-Wishart
Graph simulation
Extract links from an adjacency matrix
Graph structure comparison
Posterior probabilities of the graphs
Estimated posterior link probabilities
Plot function for S3 class "bdgraph"
Plot function for S3 class "graph"
Plot function for S3 class "sim"
Convergence plot
ROC plot
Posterior Predictive Samples
Estimated precision matrix
Predict function for S3 class "bdgraph"
Print function for S3 class "bdgraph"
Print function for S3 class "sim"
Sampling from G-Wishart distribution
Generate data from the multivariate Normal distribution
Build a ROC curve
Sampling from Wishart distribution
Graph selection
Compute the sparsity of a graph
Summary function for S3 class "bdgraph"
Trace plot of graph size
transfer for count data
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) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, and Mohammadi et al. (2023) <doi:10.48550/arXiv.2307.00127>.