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The olsrr package provides following tools for building OLS regression models using R:

  • Comprehensive Regression Output
  • Variable Selection Procedures
  • Heteroskedasticity Tests
  • Collinearity Diagnostics
  • Model Fit Assessment
  • Measures of Influence
  • Residual Diagnostics
  • Variable Contribution Assessment


olsrr uses consistent prefix ols_ for easy tab completion.

olsrr is built with the aim of helping those users who are new to the R language. If you know how to write a formula or build models using lm, you will find olsrr very useful. Most of the functions use an object of class lm as input. So you just need to build a model using lm and then pass it onto the functions in olsrr. Below is a quick demo:

Stepwise Regression

Build regression model from a set of candidate predictor variables by entering and removing predictors based on p values, in a stepwise manner until there is no variable left to enter or remove any more.

Breusch Pagan Test

Breusch Pagan test is used to test for herteroskedasticity (non-constant error variance). It tests whether the variance of the errors from a regression is dependent on the values of the independent variables. It is a (\chi^{2}) test.

Getting Help

If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.