Read and write labelled sparse matrices in text format as used by
    software such as 'SVMLight', 'LibSVM', 'ThunderSVM', 'LibFM', 'xLearn', 'XGBoost', 'LightGBM',
    and others. Supports labelled data for regression, classification (binary, multi-class, multi-label),
    and ranking (with 'qid' field), and can handle header metadata and comments in files.

Function to read and write the 'Stata' file format.

Functions for importing and handling text files and formatted text
    files with additional meta-data, such including '.csv', '.tab', '.json', '.xml',
    '.html', '.pdf', '.doc', '.docx', '.rtf', '.xls', '.xlsx', and others.

'Praat' <https://www.fon.hum.uva.nl/praat/> is a widely 
    used tool for manipulating, annotating and analyzing speech and 
    acoustic data. It stores annotation data in a format called a 
    'TextGrid'. This package provides a way to read these 
    files into R.

Import excel files into R. Supports '.xls' via the embedded
    'libxls' C library <https://github.com/libxls/libxls> and '.xlsx' via
    the embedded 'RapidXML' C++ library <https://rapidxml.sourceforge.net/>.
    Works on Windows, Mac and Linux without external dependencies.

A template model module, tools to help find model modules
    derived from this template and a programming syntax to use these
    modules in health economic analyses.  These elements are the
    foundation for a prototype software framework for developing living
    and transferable models and using those models in reproducible health
    economic analyses. The software framework is extended by other R
    libraries.  For detailed documentation about the framework and how to
    use it visit <https://www.ready4-dev.com/>. For a background to the
    methodological issues that the framework is attempting to help solve,
    see Hamilton et al. (2024) <doi:10.1007/s40273-024-01378-8>.

Provides a flexible and streamlined pipeline for formatting, analyzing, and 
    visualizing omics data, regardless of omics type (e.g. transcriptomics, proteomics, 
    metabolomics). The package includes tools for shaping input data into analysis-ready 
    structures, fitting linear or mixed-effect models, extracting key contrasts, and 
    generating a rich variety of ready-to-use publication-quality plots. 
    Designed for transparency and reproducibility across a wide range of study designs, 
    with customizable components for statistical modeling.