Research

My research focuses on missing data methodology, with an emphasis on imputation algorithms. I’m part of the Missing Data research group at Utrecht University, and software developer in the Amices team.

You can reach out to me if you have any questions about imputation with the R package {mice}, or if you want to collaborate on a project. I also offer workshops on missing data methodology, open science, and reproducible data science. And I’m available for consultation projects on imputing incomplete data, see our department website on statistical consulting or directly.

I aim to work open source and make all of my research repositories publicly available, for example on GitHub or the OSF. For an overview of my published work, see my ORCID page. Other places to find my research output are my CV, Utrecht University page, and Google Scholar.

Development

{ggmice} package

2021-current | ggmice: Visualizations for mice with ggplot2 | R package with vignette on CRAN

2019-current | shinymice: Interactive evaluation for mice with shiny | R shiny web app with demo version and (upcoming) R package

Selected contributions

2024 | Toward a standardized evaluation of imputation methodology | Review article published in Biometrical Journal

2023 | Quality control, data cleaning, imputation | Book chapter in ‘Clinical Applications of Artificial Intelligence in Real-World Data’, available from ArXiv

2020 | Missing the point: non-convergence in iterative imputation algorithms | Conference paper presented at ICML, available from ArXiv