Designed to streamline data analysis and statistical testing, reducing the length of R 
    scripts while generating well-formatted outputs in 'pdf', 'Microsoft Word', and 'Microsoft Excel' 
    formats. In essence, the package contains functions which are sophisticated wrappers around 
    existing R functions that are called by using 'f_' (user f_riendly) prefix followed by the normal 
    function name. This third version of the 'rfriend' package focuses  primarily on data exploration, 
    including tools for creating summary tables, f_summary(), summary figures, f_scan(), 
    outlier detection and removal, f_outlier() and f_remove_outliers(), performing data transformations,
    f_boxcox() in part based on 'MASS/boxcox' and 'rcompanion', and f_bestNormalize() 
    which wraps and extends functionality from the 'bestNormalize' package. Furthermore, 'rfriend' 
    can automatically (or on request) generate visualizations such as boxplots, f_boxplot(), 
    QQ-plots, f_qqnorm(), histograms f_hist(), and density plots f_density(). Additionally, the package 
    includes several statistical test functions: f_aov(), f_chisq_test(), f_corplot(), f_kruskal_test(), 
    f_lmer(), f_glm(), f_t_test(), f_wilcox_test(),  for sequential testing and visualisation of the 
    similar named 'stats' functions. These functions, except for f_chisq_test(), support testing multiple
    response variables and predictors, while also handling assumption checks, data transformations, 
    and post hoc tests. Post hoc results are automatically summarized in a table using the compact letter 
    display (cld) format for easy interpretation. The package also provides a function to do model 
    comparison, f_model_comparison(), and several utility functions to simplify common R tasks. 
    For example, f_clear() clears the workspace and restarts R with a single command; f_setwd() sets 
    the working directory to match the directory of the current script; f_theme() quickly changes 
    'RStudio' themes; and f_factors() converts multiple columns of a data frame to factors, and much more. 
    If you encounter any issues or have feature requests, please feel free to contact me via email.