`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, 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.

remotes::install_github("statdivlab/rigr")

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

rigr is maintained by the StatDivLab, 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!

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

Authors: Scott S Emerson, Brian D Williamson, Charles Wolock, Taylor Okonek, Yiqun T Chen, Jim Hughes, Amy Willis, Andrew J Spieker and Travis Y Hee Wai.

Issues

If you encounter any bugs, please file an issue. Better yet, submit a pull request!