Key attributes of a modern statistical computing tool

TL;DR

This paper proposes a comprehensive set of attributes for modern statistical computing tools, emphasizing accessibility, flexibility, reproducibility, and support for both novice and expert users to improve statistical analysis and learning.

Contribution

It introduces a broad, applicable framework of attributes for evaluating and guiding the development of modern statistical computing tools.

Findings

Framework covers accessibility, data focus, interactivity, documentation, and extensibility.

Guidelines aim to unify tools for novices and experts, enhancing support and usability.

Encourages cross-level tool development to bridge gaps in statistical computing environments.

Abstract

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.