Key Attributes of a Modern Statistical Computing Tool.
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 reasses...
| Publicado en: | American Statistician Vol. 73; no. 4; pp. 375 - 385 |
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| Formato: | Artículo |
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Taylor & Francis Ltd
Nov2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=139505104&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 139505104 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Nov2019 vid: 73 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 139505104 10.1080/00031305.2018.1482784 ppf: 375 ppct: 10 formats: tig: atl: Key Attributes of a Modern Statistical Computing Tool. aug: au: McNamara, Amelia affil: Statistical and Data Sciences, Smith College, Northampton, MA su: Computer software development Data science Design software Statisticians sug: subj: Custom Computer Programming Services Computer systems design and related services (except video game design and development) Computer software development Data science Design software Statisticians keyword: Bootstrap Data visualization Exploratory data analysis Randomization Reproducibility Software design Software evaluation Bootstrap Data visualization Exploratory data analysis Randomization Reproducibility Software design Software evaluation ab: 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 article 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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