Converting structured data from tables into XML format using
    predefined templates ensures consistency and flexibility, making it
    ideal for data exchange, reporting, and automated workflows.

Calculates the robust Taba linear, Taba rank (monotonic), TabWil, and TabWil rank 
    correlations. Test statistics as well as one sided or two sided p-values are provided 
    for all correlations. Multiple correlations and p-values can be calculated 
    simultaneously across multiple variables. In addition, users will have the option to use 
    the partial, semipartial, and generalized partial correlations; where the partial and 
    semipartial correlations use linear, logistic, or Poisson regression to modify the specified
    variable.

Produces weighted cross-tabulation tables for one or more outcome 
    variables across one or more breakdown variables, and exports them directly 
    to 'Excel'. For each outcome-by-breakdown combination, the package creates a 
    weighted percentage table and a corresponding unweighted count table, with 
    transparent handling of missing values and light, readable formatting. 
    Designed to support social survey analysis workflows that require large sets of 
    consistent, publication-ready tables.

Functions for tabulating and summarising categorical variables. 
    Most functions are designed to work with dataframes, and use the 'tidyverse' 
    idiom of taking the dataframe as the first argument so they work within 
    pipelines. Equivalent functions that operate directly on vectors are also 
    provided where it makes sense. This package aims to make exploratory data 
    analysis involving categorical variables quicker, simpler and more robust.

Sometimes you need to split your data and work on the two chunks independently before bringing them back together. 'Taber' allows you to do that with its two functions.

Simple tabulation should be dead simple. 
    This package is an opinionated approach to easy tabulations while also 
    providing exact numbers and allowing for re-usability. 
    This is achieved by providing tabulations as data.frames with columns for 
    values, optional variable names, frequency counts including and excluding 
    NAs and percentages for counts including and excluding NAs. Also values are
    automatically sorted by in decreasing order of frequency counts to allow for 
    fast skimming of the most important information.