An optimal alternating optimization algorithm for estimation of precision matrices of sparse tensor graphical models, and an efficient inference procedure for support recovery of the precision matrices.

Provides a comprehensive suite of statistical tools for analyzing, simulating, and computing properties of the Topp-Leone Cauchy Rayleigh (TLCAR) distribution, a versatile distribution amalgamating features of the Topp-Leone, Cauchy, and Rayleigh distributions, ideal for modeling intricate, heterogeneous data across scientific domains. See Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2023) <doi:10.1007/s44199-023-00066-4> and Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2024) <doi:10.1007/s44199-023-00069-1> for further insights.

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.

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>.