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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, 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 (8b901ee9) 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, 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!

Do you have a question? Please first check out the vignettes, then please post on the Discussions.

Code of Conduct

Please note that the rigr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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Version

Install

install.packages('rigr')

Monthly Downloads

399

Version

1.0.7

License

MIT + file LICENSE

Maintainer

Amy D Willis

Last Published

April 18th, 2025

Functions in rigr (1.0.7)

ttesti

T-test Given Summary Statistics with Improved Layout
ttest

T-test with Improved Layout
salary

Salary dataset
rstudent.uRegress

Extract Studentized residuals from uRegress objects
polynomial

Create Polynomials
wilcoxon

Wilcoxon Signed Rank and Mann-Whitney-Wilcoxon Rank Sum Test
cooks.distance.uRegress

Calculate Cook's distances from uRegress objects
U

Create a Partial Formula
dummy

Create Dummy Variables
anova.uRegress

ANOVA
hatvalues.uRegress

Calculate the hat-values (leverages) from uRegress objects
dfbeta.uRegress

Calculate dfbeta from uRegress objects
fev

FEV dataset
descrip

Descriptive Statistics
lincom

Tests of Linear Combinations of Regression Coefficients
dfbetas.uRegress

Calculate dfbetas from uRegress objects
regress

General Regression for an Arbitrary Functional
proptest

Test of proportions with improved layout
rstandard.uRegress

Extract standardized residuals from uRegress objects
proptesti

Test of proportions from summary statistics
predict.uRegress

Prediction Intervals for uRegress objects
rigr-package

Regression, Inference, and General Data Analysis Tools in R
psa

PSA dataset
mri

MRI dataset
residuals.uRegress

Extract Residuals from uRegress objects