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.

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>.

Computation of effects under linear, logistic and Poisson regression
    models with transformed variables. Logarithm and power transformations are allowed.
    Effects can be displayed both numerically and graphically in both the original
    and the transformed space of the variables. The methods are described in Barrera-Gomez
    and Basagana (2015) <doi:10.1097/EDE.0000000000000247>.

Calculates empirical TL-moments (trimmed L-moments) of arbitrary 
    order and trimming, and converts them to distribution parameters.

Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing	relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. 
	References used for this package:
	Foley, James, Andries van Dam, Steven Feiner, and John Hughes. 
	"Computer Graphics: Principle and Practice". Addison-Wesley 
	Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1);
	Genz, A., "Numerical computation of multivariate
	normal probabilities," Journal of Computational and
	Graphical Statistics, 1, 141-149 (1992) <doi:10.1080/10618600.1992.10477010>;
    Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low
    Rank Covariance Structures for Computing High-Dimensional Normal and Student-
    t Probabilities," Statistics and Computing, 31.1, 1-16 (2021) 
    <doi:10.1007/s11222-020-09978-y>;
    Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: 
    Computing High-Dimensional Multivariate Normal and Student-t Probabilities 
    with Low-Rank Methods in R," Journal of Statistical Software, 101.4, 
    1-25 (2022) <doi:10.18637/jss.v101.i04>.