Provides step-by-step derivations of key results in
    mathematical statistics, including transformations of random variables,
    order statistics, and sampling distributions. The package combines
    analytical derivation with Monte Carlo simulation to compare theoretical
    and empirical results, facilitating deeper understanding of statistical
    theory and its computational implementation. The methods are motivated
    by standard treatments in mathematical statistics (Hogg, McKean, and
    Craig, 2019, ISBN: 9780134686991).

A toolkit for common statistical analyses including descriptive statistics, Student's t-tests (one-sample, independent, and paired), one-way and two-way Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA), chi-square tests, Fisher's Exact Test, McNemar's Test, correlation analysis, simple and multiple linear regression, logistic regression, Friedman Test, and non-parametric tests (Mann-Whitney U, Wilcoxon Signed Rank, and Kruskal-Wallis). Additional tools include statistical power analysis and automated assumption checking. Each function automatically interprets results in plain English, reporting effect sizes, confidence intervals, and p-value interpretations. Post-hoc tests are automatically applied following significant results. A master function automatically detects the appropriate test based on the structure of the input data. Methods are based on Cohen, J. (1988) <doi:10.4324/9780203771587>, Tukey, J. W. (1949) <doi:10.2307/3001913>, and Shapiro and Wilk (1965) <doi:10.2307/2333709>.

The 'cartogram' heatmaps generated by the included methods 
    are an alternative to choropleth maps for the United States
    and are based on work by the Washington Post graphics department in their report
    on "The states most threatened by trade" 
    (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>).
    "State bins" preserve as much of the geographic placement of the states as 
    possible but have the look and feel of a traditional heatmap. Functions are 
    provided that allow for use of a binned, discrete scale, a continuous scale 
    or manually specified colors depending on what is needed for the underlying data.