Empirical Bayes Methods for Pharmacovigilance

Documentation for package ‘pvEBayes’ version 0.1.1

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