# Empirical Bayes Methods for Pharmacovigilance

## Documentation for package ‘pvEBayes’ version 0.2.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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