Package: MDDC 1.1.0

Anran Liu

MDDC: Modified Detecting Deviating Cells Algorithm in Pharmacovigilance

Methods for detecting signals related to (adverse event, medical product e.g. drugs, vaccines) pairs, a data generation function for simulating pharmacovigilance datasets, and various utility functions. For more details please see Liu A., Mukhopadhyay R., and Markatou M. <doi:10.48550/arXiv.2410.01168>.

Authors:Anran Liu [aut, cre], Raktim Mukhopadhyay [aut], Marianthi Markatou [aut]

MDDC_1.1.0.tar.gz
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MDDC_1.1.0.tgz(r-4.6-x86_64)MDDC_1.1.0.tgz(r-4.6-arm64)MDDC_1.1.0.tgz(r-4.5-x86_64)MDDC_1.1.0.tgz(r-4.5-arm64)
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MDDC_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
MDDC/json (API)

# Install 'MDDC' in R:
install.packages('MDDC', repos = c('https://niuniular.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/niuniular/mddc/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • betablocker500 - FDA dataset for beta blockers with 500 adverse events
  • 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

On CRAN:

Conda:

pharmacovigilancecpp

4.18 score 3 stars 4 scripts 145 downloads 10 exports 24 dependencies

Last updated from:1b7975d684. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK148
linux-devel-x86_64OK151
source / vignettesOK215
linux-release-arm64OK139
linux-release-x86_64OK148
macos-release-arm64OK89
macos-release-x86_64OK223
macos-oldrel-arm64OK191
macos-oldrel-x86_64OK286
windows-develOK146
windows-releaseOK138
windows-oldrelOK125
wasm-releaseOK107

Exports:check_and_fix_contin_tablefind_optimal_coefgenerate_contin_table_with_clustered_AEgenerate_contin_table_with_clustered_AE_with_tolget_expected_countsget_std_pearson_resmddc_boxplotmddc_mcplot_heatmapreport_drug_AE_pairs

Dependencies:clicodetoolscpp11doParallelfarverforeachggplot2gluegtableisobanditeratorslabelinglifecycleMASSR6RColorBrewerRcppRcppEigenrlangS7scalesvctrsviridisLitewithr

Usage Examples for MDDC in R

Rendered fromIntroduction_to_MDDC.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2025-04-05
Started: 2024-08-20

Readme and manuals

Help Manual

Help pageTopics
Modified Detecting Deviating Cells Algorithm in PharmacovigilanceMDDC-package MDDC
FDA dataset for beta blockers with 500 adverse eventsbetablocker500
Verifying and correcting the input I by J contingency tablecheck_and_fix_contin_table
Find Adaptive Boxplot Coefficient `coef` via Grid Searchfind_optimal_coef
Generate simulated contingency tables with the option of incorporating adverse event correlation within clusters.generate_contin_table_with_clustered_AE
Generate simulated contingency tables with the option of incorporating adverse event correlation within clusters and tolerance for total report count.generate_contin_table_with_clustered_AE_with_tol
Compute the Expected Count Matrix from a Contingency Tableget_expected_counts
Computing the standardized Pearson residuals for a given I \times J contingency tableget_std_pearson_res
Modified Detecting Deviating Cells (MDDC) algorithm for adverse event signal identification with boxplot method for cutoff selection.mddc_boxplot
Modified Detecting Deviating Cells (MDDC) algorithm for adverse event signal identification with Monte Carlo (MC) method for cutoff selection.mddc_mc
Plot Heatmapplot_heatmap
Report the potential adverse events for drugs from contingency tablereport_drug_AE_pairs
FDA dataset for sedatives with 1000 adverse eventssedative1000
FDA statin dataset with 101 adverse eventsstatin101
FDA statin dataset with 49 adverse eventsstatin49
Cluster index of the FDA statin dataset with 49 adverse eventsstatin49_AE_idx