  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.
