Package index
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AIC(<pvEBayes>) - Obtain Akaike Information Criterion (AIC) for a pvEBayes object
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BIC(<pvEBayes>) - Obtain Bayesian Information Criterion (BIC) for a pvEBayes object
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estimate_null_expected_count() - Estimate expected null baseline count based on reference row and column
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extract_all_fitted_models() - Extract all fitted models from a tuned pvEBayes Object
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eyeplot_pvEBayes() - Generate an eyeplot showing the distribution of posterior draws for selected drugs and adverse events
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gbca2025 - FDA GBCA dataset with 1328 adverse events
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gbca2025_69 - FDA GBCA dataset with 69 adverse events
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generate_contin_table() - Generate random contingency tables based on a reference table embedded signals,and possibly with zero inflation
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heatmap_pvEBayes() - Generate a heatmap plot visualizing posterior probabilities for selected drugs and adverse events
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logLik(<pvEBayes>) - Extract log marginal likelihood for a pvEBayes object
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plot(<pvEBayes>) - Plotting method for a pvEBayes object
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posterior_draws() - Generate posterior draws for each AE-drug combination
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print(<pvEBayes>) - Print method for a pvEBayes object
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pvEBayes-package - A suite of empirical Bayes methods to use in pharmacovigilance.
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pvEBayes() - Fit a general-gamma, GPS, K-gamma, KM or efron model for a contingency table.
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pvEBayes_tune() - Select hyperparameter and obtain the optimal general-gamma or efron model based on AIC and BIC
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statin2025 - FDA statin dataset with 5119 adverse events
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statin2025_44 - FDA statin dataset with 44 adverse events
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statin42 - FDA statin dataset with 42 adverse events
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summary(<pvEBayes>) - Summary method for a pvEBayes object