Shiny interfaces and graphical functions for multivariate analysis results exploration.

Conduct numerous exploratory analyses in an instant with a 
    point-and-click interface. With one simple command, this tool 
    launches a Shiny App on the local machine. Drag and drop variables 
    in a data set to categorize them as possible independent, 
    dependent, moderating, or mediating variables. Then run dozens 
    (or hundreds) of analyses instantly to uncover any statistically 
    significant relationships among variables. Any relationship 
    thus uncovered should be tested in follow-up studies. 
    This tool is designed only to facilitate exploratory 
    analyses and should NEVER be used for p-hacking. Many of 
    the functions used in this package are previous versions of functions
    in the R Packages 'kim' and 'ezr'.
    Selected References:
    Chang et al. (2021) <https://CRAN.R-project.org/package=shiny>.
    Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>.
    Kim (2023) <https://jinkim.science/docs/kim.pdf>.
    Kim (2021) <doi:10.5281/zenodo.4619237>.
    Kim (2020) <https://CRAN.R-project.org/package=ezr>.
    Simmons et al. (2011) <doi:10.1177/0956797611417632>
    Tingley et al. (2019) <https://CRAN.R-project.org/package=mediation>.
    Wickham et al. (2020) <https://CRAN.R-project.org/package=ggplot2>.

Interactive data exploration with one line of code, automated
    reporting or use an easy to remember set of tidy functions for low
    code exploratory data analysis.

Simplifies some complicated and labor intensive processes involved in exploring and explaining data.  Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data.  Also includes some convenience features designed to remove as much redundant typing as possible.

Functions to profile a dataset, identify anomalies (special values, outliers, and inliers, 
                 defined as data values that are repeated unusually often), and compare data subsets 
                 with respect to either numerical or categorical variable distributions.