Articles in the 'R Journal' were first authored in 'LaTeX', which performs 
    admirably for 'PDF' files but is less than ideal for modern online interfaces.
    The 'texor' package does all the transitional chores and conversions necessary
    to move to the online versions.

An extension of ExPosition for two table analyses, specifically, discriminant analyses.

Compile snippets of 'LaTeX' directly into images
    from the R console to view in the 'RStudio' viewer pane, Shiny apps
    and 'RMarkdown' documents.

Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.)

Link R with Transformers from Hugging Face to transform text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see  <https://www.r-text.org>.

Find similarities between texts using the Smith-Waterman algorithm. The algorithm performs local sequence alignment and determines similar regions between two strings.
    The Smith-Waterman algorithm is explained in the paper: "Identification of common molecular subsequences" by T.F.Smith and M.S.Waterman (1981), available at <doi:10.1016/0022-2836(81)90087-5>. 
    This package implements the same logic for sequences of words and letters instead of molecular sequences.

Allows users to analyze text and classify emotions
    such as happiness, sadness, anger, fear, and neutrality.
    It combines text preprocessing, TF-IDF (Term Frequency-Inverse Document Frequency) 
    feature extraction, and Random Forest classification to predict emotions
    and map them to corresponding emojis for enhanced sentiment visualization.