Fast and memory-friendly tools for text vectorization, topic
    modeling (LDA, LSA), word embeddings (GloVe), similarities. This package
    provides a source-agnostic streaming API, which allows researchers to perform
    analysis of collections of documents which are larger than available RAM. All
    core functions are parallelized to benefit from multicore machines.

Generate 'LaTeX' tables directly from R. It builds 'LaTeX' tables in blocks in the spirit of 'ggplot2' using the '+' and '/' operators for concatenation in the vertical and horizontal dimensions, respectively. It exports tables in the 'LaTeX' tabular environment using '.tex' code. It can compile '.tex' code to 'PDF' automatically.

It analyzes text to create a count of top n-grams, including tokens (one-word), bigrams(two-word), and trigrams (three-word), while removing all stopwords. It also plots the n-grams and corresponding counts as a bar chart.

A lightweight and focused text annotation tool built with 'shiny'. Provides an 
    interactive graphical user interface for coding text documents,
    managing code hierarchies, creating memos, and analyzing coding patterns.
    Features include code co-occurrence analysis, visualization of coding patterns,
    comparison of multiple coding sets, and export capabilities. Supports
    collaborative qualitative research through standardized annotation formats
    and analysis tools.

Compute a non-overlapping layout of text boxes to label multiple overlain curves. For each curve, iteratively search for an adjacent x,y position for the text box that does not overlap with the other curves. If this process fails, then offsets are computed to add to the y values for each curve, that results in sufficient space to add all of the text labels.

Tools to clean and process text.  Tools are geared at checking for substrings that
          are not optimal for analysis and replacing or removing them (normalizing) with more
          analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards
          (2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For
          example, emoticons are often used in text but not always easily handled by analysis
          algorithms.  The replace_emoticon() function replaces emoticons with word
          equivalents.