Support functions and datasets to facilitate the analysis of linguistic
    data. The current focus is on the calculation of corpus-linguistic dispersion
    measures as described in Gries (2021) <doi:10.1007/978-3-030-46216-1_5> and 
    Soenning (2025) <doi:10.3366/cor.2025.0326>. The most commonly used parts-based 
    indices are implemented, including different formulas and modifications that are 
    found in the literature, with the additional option to obtain frequency-adjusted 
    scores. Dispersion scores can be computed based on individual count variables or 
    a term-document matrix.

Gives a number of functions to aid common data 
    analysis processes and reporting statistical results in an 'RMarkdown' file. 
    Data analysis functions combine multiple base R functions used to describe 
    simple bivariate relationships into a single, easy to use function. 
    Reporting functions will return character strings to report p-values, 
    confidence intervals, and hypothesis test and regression results. Strings 
    will be LaTeX-formatted as necessary and will knit pretty in an 'RMarkdown' 
    document. The package also provides wrappers function in the 'tableone' 
    package to make the results knit-able.

Documentation for commonly-used objects included in the base distribution of 
    R. Note that 'tldrDocs' does not export any functions itself, its purpose is
    to write .Rd files during its installation for 'tldr()' to find.

Density, distribution function, quantile function, and random generating function of the Topp–Leone Garima distribution
    based on Boonmeekham, A., Supapakorn, T., & Bodhisuwan, W. (2025)<doi:10.1134/S1995080225608471>.
    In addition, maximum likelihood estimation for the Topp–Leone Garima distribution is provided.

This comprehensive toolkit for T-distributed regression is designated as "TLIC" (The LIC for T Distribution Regression Analysis) analysis. It is predicated on the assumption that the error term adheres to a T-distribution. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.