# R-miss-tastic: a unified platform for missing values methods and workflows

arXiv: https://arxiv.org/abs/1908.04822 · HTML: https://mlsanity.com/paper/1908.04822/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 8 of 8)

- [Transformed Distribution Matching for Missing Value Imputation](https://mlsanity.com/paper/2302.10363/) (2023)
- [Conformal Prediction with Missing Values](https://mlsanity.com/paper/2306.02732/) (2023)
- [MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values](https://mlsanity.com/paper/2311.14108/) (2023)
- [mDAE : modified Denoising AutoEncoder for missing data imputation](https://mlsanity.com/paper/2411.12847/) (2024)
- [When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values](https://mlsanity.com/paper/2507.13024/) (2025)
- [M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values](https://mlsanity.com/paper/2405.00182/) (2024)
- [Prediction Models That Learn to Avoid Missing Values](https://mlsanity.com/paper/2505.03393/) (2025)
- [Closing Gaps: An Imputation Analysis of ICU Vital Signs](https://mlsanity.com/paper/2510.24217/) (2025)

## Abstract (arXiv)

Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and visualization methods require complete data, and improper handling of missing data results in information loss or biased analyses. Since the seminal work of Rubin (1976), a burgeoning literature on missing values has arisen, with heterogeneous aims and motivations. This led to the development of various methods, formalizations, and tools. For practitioners, it remains nevertheless challenging to decide which method is most suited for their problem, partially due to a lack of systematic covering of this topic in statistics or data science curricula. To help address this challenge, we have launched the "R-miss-tastic" platform, which aims to provide an overview of standard missing values problems, methods, and relevant implementations of methodologies. Beyond gathering and organizing a large majority of the material on missing data (bibliography, courses, tutorials, implementations), "R-miss-tastic" covers the development of standardized analysis workflows. Indeed, we have developed several pipelines in R and Python to allow for hands-on illustration of and recommendations on missing values handling in various statistical tasks such as matrix completion, estimation and prediction, while ensuring reproducibility of the analyses. Finally, the platform is dedicated to users who analyze incomplete data, researchers who want to compare their methods and search for an up-to-date bibliography, and also teachers who are looking for didactic materials (notebooks, video, slides).