maars: Tidy Inference under the 'Models as Approximations' Framework in R
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maars：在“模型作为近似”框架下开展整洁化R推断
- 研究问题
- 在线性回归仅为现实近似、模型可能误设时，如何让R用户方便地开展有效推断，并明确理解各类输出的适用假设？
- 方法与思路
- 作者开发maars包，在接近lm()的工作流程中提供统一格式输出，整合多类bootstrap与子抽样，并显式返回有效性假设。
- 材料陈述的贡献
- 摘要强调统一语法、可比较的多种推断工具和显式展示假设的教学设计，将已有模型误设下的推断研究接入R工作流程。
- 阅读关注点
- 应核对各推断工具对应的目标参数、抽样条件及误设容许范围，以及包中返回的假设是否足以支持实际使用。
- 阅读边界
- 这是arXiv预印本，不能据此声称已同行评审或期刊接收。摘要未给出各工具的完整条件、性能比较或软件实现验证。
原文摘要
Linear regression using ordinary least squares (OLS) is a critical part of every statistician's toolkit. In R, this is elegantly implemented via lm() and its related functions. However, the statistical inference output from this suite of functions is based on the assumption that the model is well specified. This assumption is often unrealistic and at best satisfied approximately. In the statistics and econometrics literature, this has long been recognized and a large body of work provides inference for OLS under more practical assumptions. This can be seen as model-free inference. In this paper, we introduce our package maars ("models as approximations") that aims at bringing research on model-free inference to R via a comprehensive workflow. The maars package differs from other packages that also implement variance estimation, such as sandwich, in three key ways. First, all functions in maars follow a consistent grammar and return output in tidy format, with minimal deviation from the typical lm() workflow. Second, maars contains several tools for inference including empirical, multiplier, residual bootstrap, and subsampling, for easy comparison. Third, maars is developed with pedagogy in mind. For this, most of its functions explicitly return the assumptions under which the output is valid. This key innovation makes maars useful in teaching inference under misspecification and also a powerful tool for applied researchers. We hope our default feature of explicitly presenting assumptions will become a de facto standard for most statistical modeling in R.