R-miss-tastic：缺失值方法与工作流的统一平台
R-miss-tastic: a unified platform for missing values methods and workflows
AI总结：
本文介绍 R-miss-tastic 平台，通过整合缺失值方法资料、实现与标准化 R/Python 工作流，支持不完整数据分析、方法比较和教学。
摘要：
在处理数据时，缺失值不可避免。随着来自不同来源的数据越来越多，缺失值的出现也进一步加剧。然而，大多数统计模型和可视化方法都要求完整数据，而对缺失数据处理不当会导致信息损失或有偏分析。自 Rubin（1976）的开创性工作以来，关于缺失值的文献迅速增长，其目标和动机各不相同。这推动了各种方法、形式化表述和工具的发展。尽管如此，对于实践者而言，判断哪种方法最适合其问题仍然具有挑战性，部分原因在于统计学或数据科学课程对这一主题缺乏系统性覆盖。为帮助应对这一挑战，我们推出了“R-miss-tastic”平台，旨在概述标准缺失值问题、方法以及相关方法实现。除了收集和整理关于缺失数据的大部分资料（参考文献、课程、教程、实现）之外，“R-miss-tastic”还涵盖标准化分析工作流的开发。具体而言，我们开发了若干 R 和 Python 流水线，以便在矩阵补全、估计和预测等各类统计任务中，对缺失值处理进行实操演示并提供建议，同时确保分析的可复现性。最后，该平台面向分析不完整数据的用户、希望比较其方法并查找最新参考文献的研究人员，以及寻找教学材料（notebook、视频、幻灯片）的教师。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
