Modified Detecting Deviating Cells Algorithm in Pharmacovigilance

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Documentation for package ‘MDDC’ version 1.0.0

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

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MDDC-package	Modified Detecting Deviating Cells Algorithm in Pharmacovigilance
betablocker500	FDA dataset for beta blockers with 500 adverse events
check_and_fix_contin_table	Verifying and correcting the input I by J contingency table
find_optimal_coef	Find Adaptive Boxplot Coefficient 'coef' via Grid Search
generate_contin_table_with_clustered_AE	Generate simulated contingency tables with the option of incorporating adverse event correlation within clusters.
generate_contin_table_with_clustered_AE_with_tol	Generate simulated contingency tables with the option of incorporating adverse event correlation within clusters and tolerance for total report count.
get_expected_counts	Compute the Expected Count Matrix from a Contingency Table
get_std_pearson_res	Computing the standardized Pearson residuals for a given I \times J contingency table
MDDC	Modified Detecting Deviating Cells Algorithm in Pharmacovigilance
mddc_boxplot	Modified Detecting Deviating Cells (MDDC) algorithm for adverse event signal identification with boxplot method for cutoff selection.
mddc_mc	Modified Detecting Deviating Cells (MDDC) algorithm for adverse event signal identification with Monte Carlo (MC) method for cutoff selection.
plot_heatmap	Plot Heatmap
report_drug_AE_pairs	Report the potential adverse events for drugs from contingency table
sedative1000	FDA dataset for sedatives with 1000 adverse events
statin101	FDA statin dataset with 101 adverse events
statin49	FDA statin dataset with 49 adverse events
statin49_AE_idx	Cluster index of the FDA statin dataset with 49 adverse events