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Introduction

Authors

Maintainers

Documentation

The documentation is hosted at - https://niuniular.github.io/MDDC/index.html

Citation

If you use this package in your research or work, please cite it as follows:

@misc{liu2024mddcrpythonpackage,
      title={MDDC: An R and Python Package for Adverse Event Identification in Pharmacovigilance Data}, 
      author={Anran Liu and Raktim Mukhopadhyay and Marianthi Markatou},
      year={2024},
      eprint={2410.01168},
      archivePrefix={arXiv},
      primaryClass={stat.CO},
      url={https://arxiv.org/abs/2410.01168}, 
}

Funding Information

The work has been supported by Food and Drug Administration, and Kaleida Health Foundation.

References

Liu, A., Mukhopadhyay, R., and Markatou, M. (2024). MDDC: An R and Python package for adverse event identification in pharmacovigilance data. arXiv preprint. arXiv:2410.01168

Liu, A., Markatou, M., Dang, O., and Ball, R. (2024). Pattern discovery in pharmacovigilance through the Modified Detecting Deviating Cells (MDDC) algorithm. Technical Report, Department of Biostatistics, University at Buffalo.

Rousseeuw, P. J., and Bossche, W. V. D. (2018). Detecting deviating data cells. Technometrics, 60(2), 135-145.