Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units.  Maximum, minimum, or an allowable range of differences between units on one variable can be set.  Randomly assign units to treatment conditions.  Diagnose potential interference between units assigned to different treatment conditions.  Write outputs to .tex and .csv files.

Functions to combine data on voting blocs' 
    size, turnout, and vote choice to estimate each bloc's
    vote contributions to the Democratic and Republican parties.
    The package also includes functions for uncertainty estimation and
    plotting. Users may define voting blocs along a discrete or continuous variable.
    The package implements methods described in Grimmer, Marble, and Tanigawa-Lau (2023) <doi:10.31235/osf.io/c9fkg>.

Write blog posts and web pages in R Markdown. This package
    supports the static site generator 'Hugo' (<https://gohugo.io>) best,
    and it also supports 'Jekyll' (<https://jekyllrb.com>) and 'Hexo'
    (<https://hexo.io>).

It includes estimating the area under the concentrations
        versus time curve (AUC) and its standard error for data with
        Below the Limit of Quantification (BLOQ) observations. Two 
        approaches are implemented: direct estimation using censored maximum 
        likelihood, also by first imputing the BLOQ's 
        using various methods, then compute AUC and its standard 
        error using imputed data. Technical details can found in 
        Barnett, Helen Yvette, Helena Geys, Tom Jacobs, and Thomas Jaki. 
		"Methods for Non-Compartmental Pharmacokinetic Analysis With Observations 
		Below the Limit of Quantification." Statistics in Biopharmaceutical 
		Research (2020): 1-12.
        (available online: 
        <https://www.tandfonline.com/doi/full/10.1080/19466315.2019.1701546>).

Tools designed to make it easier for beginner and intermediate users to build and validate 
    binary logistic regression models. Includes bivariate analysis, comprehensive regression output, 
    model fit statistics, variable selection procedures, model validation techniques and a 'shiny' 
    app for interactive model building.