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Please edit that file --\u003e\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"README-\"\n)\n```\n\n# olsrr \n\n\u003c!-- badges: start --\u003e\n[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/olsrr)](https://cran.r-project.org/package=olsrr)\n[![R build status](https://github.com/rsquaredacademy/olsrr/workflows/R-CMD-check/badge.svg)](https://github.com/rsquaredacademy/olsrr/actions)\n[![Coverage status](https://codecov.io/gh/rsquaredacademy/olsrr/branch/master/graph/badge.svg)](https://app.codecov.io/github/rsquaredacademy/olsrr?branch=master) \n\u003c!-- badges: end --\u003e\n\n## Overview\n\nThe olsrr package provides following tools for building OLS regression models using R:\n\n- Comprehensive Regression Output\n- Variable Selection Procedures\n- Heteroskedasticity Tests\n- Collinearity Diagnostics\n- Model Fit Assessment\n- Measures of Influence\n- Residual Diagnostics\n- Variable Contribution Assessment\n\n## Installation\n\n```{r cran-installation, eval = FALSE}\n# Install release version from CRAN\ninstall.packages(\"olsrr\")\n\n# Install development version from GitHub\n# install.packages(\"pak\")\npak::pak(\"rsquaredacademy/olsrr\")\n```\n\n## Articles\n\n- [Quick Overview](https://olsrr.rsquaredacademy.com/articles/intro.html)\n- [Variable Selection Methods](https://olsrr.rsquaredacademy.com/articles/variable_selection.html)\n- [Residual Diagnostics](https://olsrr.rsquaredacademy.com/articles/residual_diagnostics.html)\n- [Heteroskedasticity](https://olsrr.rsquaredacademy.com/articles/heteroskedasticity.html)\n- [Measures of Influence](https://olsrr.rsquaredacademy.com/articles/influence_measures.html)\n- [Collinearity Diagnostics](https://olsrr.rsquaredacademy.com/articles/regression_diagnostics.html)\n\n## Usage\n\n```{r, echo=FALSE, message=FALSE}\nlibrary(olsrr)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(gridExtra)\nlibrary(nortest)\nlibrary(goftest)\n```\n\nolsrr uses consistent prefix `ols_` for easy tab completion. 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\na quick demo:\n\n#### Regression\n\n```{r regress}\nmodel \u003c- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)\nols_regress(model)\n```\n\n## Getting Help\n\nIf you encounter a bug, please file a minimal reproducible example using \n[reprex](https://reprex.tidyverse.org/index.html) on github. For questions and clarifications, \nuse [StackOverflow](https://stackoverflow.com/).\n\n## Code of Conduct\n\nPlease note that the olsrr project is released with a [Contributor Code of Conduct](https://olsrr.rsquaredacademy.com/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frsquaredacademy%2Folsrr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frsquaredacademy%2Folsrr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frsquaredacademy%2Folsrr/lists"}