Package index
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SSJGL_CV_final_pcorCI() - Full SSJGL workflow: CV selection + final fit with bootstrap CIs
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SSJGL_final_with_pcor_CI() - Fit SSJGL at best v0 with bootstrap confidence intervals
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SSJGL_select_v0_cv() - Select v0 via K-fold cross-validation
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coef(<ssjgl>) - Extract precision matrices from an ssjgl fit
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compute_metrics() - Compute comprehensive evaluation metrics for an ssjgl fit
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diff_edges()print(<ssjgl_diff_edges>) - Differential edges between two groups
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extract_adjacency() - Extract binary adjacency matrices from an ssjgl fit
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extract_pcor() - Extract partial correlation matrices from an ssjgl fit
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extract_precision() - Extract precision matrices from an ssjgl fit
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extract_probabilities() - Extract edge inclusion probabilities from an ssjgl fit
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fitted(<ssjgl>) - Extract partial correlations from an ssjgl fit
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getdiffmetric() - Compute differential edge metrics across groups
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getmetric() - Compute graph recovery metrics (single group)
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make_v0_ladder() - Generate a v0 ladder
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negloglik_Gaussian() - Gaussian negative log-likelihood
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plot(<ssjgl>) - Plot partial correlation heatmaps from an ssjgl fit
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plot(<ssjgl_diff_edges>) - Plot differential edges
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plot_path() - Plot the solution path
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plot_stability() - Plot stability of graph structure across the v0 ladder
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print(<ssjgl>) - Print an ssjgl object
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print(<summary.ssjgl>) - Print summary of ssjgl
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simdat - Simulated three-group network data
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simulate_ssjgl_data() - Simulate data from known precision matrices for multiple groups
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pip_table(<ssjgl>)pcor_estimate(<ssjgl>) - Shared-format accessors for ssjgl fits
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ssjgl() - Bayesian Spike-and-Slab Joint Graphical Lasso
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summary(<ssjgl>) - Summarize an ssjgl object