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README.Rmd
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README.Rmd
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---
output: github_document
bibliography: inst/REFERENCES.bib
---
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# `maars` - an `R` implementation of Models As Approximations
The goal of the `maars` package is to implement the *Models As Approximations* series of
statistics papers [@buja2019modelsasapproximationspart1] and [@buja2019modelsasapproximationspart2]. This package was inspired by the fantastic series of
lectures by [Prof. Arun Kumar Kuchibhotla](https://arun-kuchibhotla.github.io/)
and [Prof. Andreas Buja](http://www-stat.wharton.upenn.edu/~buja/),
as part of the __"STAT 36761: Modern Linear Regression"__ course
at Carnegie Mellon University (CMU) in Fall 2020.
## Installation and User Guide
To get a bug fix or to use a feature from the development version,
you can install the development version of `maars` from `GitHub`,
as follows:
```{r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("shamindras/maars")
```
More detailed instructions and user guides can be found at the [official package
website](https://shamindras.github.io/maars/). The source code for the `maars`
package can be [found on github](https://github.com/shamindras/maars).
## Citation
If you are in `R` you can simply run the following command to get the `BibTeX`
citation for `maars`:
```{r, eval=FALSE}
citation("maars")
```
Alternatively, please use the following `BibTeX` citation:
```{bibtex, eval=FALSE}
@misc{fogliato2021maars,
title = {maars: Tidy Inference under the 'Models as Approximations' Framework in R},
author = {Riccardo Fogliato and Shamindra Shrotriya and Arun Kumar Kuchibhotla},
year = {2021},
eprint = {arXiv:2106.11188},
url = {https://shamindras.github.io/maars/},
note = {R package version 0.3.0}
}
```
## Code of Conduct
Please note that the `maars` project is released with a [Contributor Code of
Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By
contributing to this project, you agree to abide by its terms.
## Related Packages
While `maars` has it's own approach and API for performing valid inference
under model misspecification for OLS, it may not meet your particular needs.
Here is a listing of other leading `R` packages in this field which you may
want to try, with links to their project pages (listed alphabetically):
- [`{car}`](https://cran.r-project.org/web/packages/car/index.html)
- [`{clubSandwich}`](https://github.com/jepusto/clubSandwich)
- [`{estimatr}`](https://declaredesign.org/r/estimatr/articles/estimatr-in-the-tidyverse.html)
- [`{lmtest}`](https://cran.r-project.org/web/packages/lmtest/index.html)
- [`{sandwich}`](http://sandwich.r-forge.r-project.org/)
## Credits
This package is developed and maintained by:
- [Riccardo Fogliato](http://www.stat.cmu.edu/~rfogliat/)
- [Shamindra Shrotriya](https://www.shamindras.com/)
- [Arun Kumar Kuchibhotla](https://arun-kuchibhotla.github.io/)
We want this to be a community project, so please feel free to contact us,
or file an issue if you would like to contribute to it.
## References