equations. The olr() returns the equation which has the greatest adjusted R-squared term or the greatest R-squared term based
on the user's discretion. Essentially, the olr() returns the best fit equation out of all the possible equations. R-squared increases
with the addition of an explanatory variable whether it is 'significant' or not, thus this was developed to eliminate that conundrum.
Adjusted R-squared is preferred to overcome this phenomenon, but each combination will still produce different results and this will
return the best one. Complimentary functions are included which list all of the equations, all of the equations in ascending order,
a function to give the user a specific model's summary, and the list of adjusted R-squared terms & R-squared terms.
A 'Python' version is available at: <https://pypi.org/project/olr/>.

Title: Tools for Teaching and Learning OLS Regression
Description: Tools for teaching and learning ordinary least squares regression. Includes
comprehensive regression output, heteroskedasticity tests, collinearity diagnostics,
residual diagnostics, measures of influence, model fit assessment and variable selection procedures.
Imports: ggplot2, gridExtra, graphics, dplyr, magrittr, purrr, tibble,

Title: Tools for Building OLS Regression Models
Description: Tools for building OLS regression models . Includes
Imports: dplyr, ggplot2, goftest, graphics, gridExtra, magrittr,
nortest, purrr, Rcpp, shiny, tibble, tidyr, stringr, recipes,
Suggests: caret, covr, descriptr, grid, haven, jsonlite, knitr,
lubridate, readr, readxl, rmarkdown, scales, shinyBS,

Imports: cli, clisymbols, crayon, dplyr, ggplot2, gh, glue, goftest,
graphics, gridExtra, magrittr, nortest, purrr, Rcpp, recipes,

Imports: car, checkmate, cli, clisymbols, crayon, dplyr, ggplot2, gh,
glue, goftest, graphics, gridExtra, magrittr, nortest, purrr,
Rcpp, recipes, rlang, shiny, stringr, tibble, tidyr

Description: Tools designed to make it easier for users, particularly beginner/intermediate R users
to build ordinary least squares regression models. Includes comprehensive regression output,
heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence,
model fit assessment and variable selection procedures.
Rcpp, recipes (>= 0.1.4), rlang, shiny, stringr, tibble, tidyr