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Linear regression using ordinary least squares (OLS) is a critical part ofevery statistician's toolkit. In R, this is elegantly implemented via lm() andits related functions. However, the statistical inference output from thissuite of functions is based on the assumption that the model is well specified.This assumption is often unrealistic and at best satisfied approximately. Inthe statistics and econometrics literature, this has long been recognized and alarge body of work provides inference for OLS under more practical assumptions.This can be seen as model-free inference. In this paper, we introduce ourpackage maars ("models as approximations") that aims at bringing research onmodel-free inference to R via a comprehensive workflow. The maars packagediffers from other packages that also implement variance estimation, such assandwich, in three key ways. First, all functions in maars follow a consistentgrammar and return output in tidy format, with minimal deviation from thetypical lm() workflow. Second, maars contains several tools for inferenceincluding empirical, multiplier, residual bootstrap, and subsampling, for easycomparison. Third, maars is developed with pedagogy in mind. For this, most ofits functions explicitly return the assumptions under which the output isvalid. This key innovation makes maars useful in teaching inference undermisspecification and also a powerful tool for applied researchers. We hope ourdefault feature of explicitly presenting assumptions will become a de factostandard for most statistical modeling in R.

maars: Tidy Inference under the 'Models as Approximations' Framework in R