现代统计计算工具的关键属性
Key attributes of a modern statistical computing tool
摘要：
在20世纪90年代，统计学家开始以原则性的方式思考计算如何更好地支持统计学的学习和实践。从那时起，软件开发的步伐加快了，计算和数据科学的进步已经改变了目标，现在是时候重新评估了。软件继续被开发，以帮助做和学习统计，但很少有关键的评价所产生的工具，也没有公认的框架来批评他们。本文提出了一套现代统计计算工具所必需的属性。该框架旨在广泛适用于新手和专家用户，特别侧重于创造更具支持性的统计计算环境。现代统计计算工具应该是可访问的，提供方便的输入，将数据作为一阶对象，支持探索性和验证性分析，允许灵活的绘图创建，支持随机化，是交互式的，包括固有的文档，支持叙述，出版和再现性，并灵活扩展。理想的情况是，所有这些属性都可以纳入一个工具，支持各级用户，但更合理的目标是为新手和专业人员设计的工具“跨越鸿沟”，从彼此的长处中汲取灵感。
英文摘要：
In the 1990s, statisticians began thinking in a principled way about how computation could better support the learning and doing of statistics. Since then, the pace of software development has accelerated, advancements in computing and data science have moved the goalposts, and it is time to reassess. Software continues to be developed to help do and learn statistics, but there is little critical evaluation of the resulting tools, and no accepted framework with which to critique them. This paper presents a set of attributes necessary for a modern statistical computing tool. The framework was designed to be broadly applicable to both novice and expert users, with a particular focus on making more supportive statistical computing environments. A modern statistical computing tool should be accessible, provide easy entry, privilege data as a first-order object, support exploratory and confirmatory analysis, allow for flexible plot creation, support randomization, be interactive, include inherent documentation, support narrative, publishing, and reproducibility, and be flexible to extensions. Ideally, all these attributes could be incorporated into one tool, supporting users at all levels, but a more reasonable goal is for tools designed for novices and professionals to `reach across the gap,' taking inspiration from each others' strengths.
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。
