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# `rigr`: Regression, Inference, and General Data Analysis Tools for R

## Introduction

`rigr` is an `R` package to streamline data analysis in `R`. Learning
both `R` and introductory statistics at the same time can be
challenging, and so we created `rigr` to facilitate common data analysis
tasks and enable learners to focus on statistical concepts.

`rigr`, formerly known as
[`uwIntroStats`](https://CRAN.R-project.org/package=uwIntroStats),
provides easy-to-use interfaces for descriptive statistics, one- and
two-sample inference, and regression analyses. `rigr` output includes
key information while omitting unnecessary details that can be confusing
to beginners. Heteroskedasticity-robust (“sandwich”) standard errors are
returned by default, and multiple partial F-tests and tests for
contrasts are easy to specify. A single regression function
(`regress()`) can fit both linear models, generalized linear models, and
proportional hazards models, allowing students to more easily make
connections between different classes of models.

## Installation

You can install the stable release of `rigr` from CRAN as follows:

    install.packages("rigr")

You can install the development version of `rigr` from GitHub using the
code below. The installment is through the `R` package `remotes`.

    #> Using GitHub PAT from the git credential store.
    #> Skipping install of 'rigr' from a github remote, the SHA1 (aacffec2) has not changed since last install.
    #>   Use `force = TRUE` to force installation

If this produces an error, please run `install.packages("remotes")`
first then try the above line again.

`rigr` is maintained by the [StatDivLab](https://statdivlab.github.io/),
but relies on community support to log issues and implement new
features. Is there a method you would like to have implemented? Please
submit a pull request or start a
[discussion](https://github.com/statdivlab/rigr/discussions/)!

## Documentation

Examples of how to use the main functions in `rigr` are provided in
three vignettes. One details the `regress` function and its utilities,
one details the `descrip` function for descriptive statistics, and the
third details functions used for one- and two-sample inference,
including `ttest`, `wilcoxon`, and `proptest`.

## Humans

Maintainer: [Amy Willis](https://statdivlab.github.io/)

Authors: [Scott S Emerson](http://www.emersonstatistics.com/), [Brian D
Williamson](https://bdwilliamson.github.io/), [Charles
Wolock](https://cwolock.github.io/), [Taylor
Okonek](https://taylorokonek.github.io/), [Yiqun T
Chen](https://yiqunchen.github.io/), [Jim
Hughes](https://www.biostat.washington.edu/people/james-hughes), [Amy
Willis](https://statdivlab.github.io/), [Andrew J
Spieker](https://www.vumc.org/biostatistics/person/andrew-spieker) and
Travis Y Hee Wai.

## Issues

If you encounter any **bugs**, please [file an
issue](https://github.com/statdivlab/rigr/issues/). Better yet, [submit
a pull request](https://github.com/statdivlab/rigr/pulls/)!

Do you have a **question**? Please first check out the vignettes, then
please post on the
[Discussions](https://github.com/statdivlab/rigr/discussions/).

## Code of Conduct

Please note that the rigr project is released with a [Contributor Code
of Conduct](https://statdivlab.github.io/rigr/CODE_OF_CONDUCT.html). By
contributing to this project, you agree to abide by its terms.

## Citation

If you use `rigr` for your analysis, please cite our paper:

Chen, Y. T., et al. (2022). rigr: Regression, Inference, and General
Data Analysis Tools in R. *Journal of Open Source Software*, 7(80),
4847. <https://doi.org/10.21105/joss.04847>.
