Create variable width bar charts i.e.
    "bar mekko" charts to include important quantitative context.
    Closely related to mosaic, spine (or spinogram), matrix,
    submarine, olympic, Mondrian or product plots and tree maps.

In the MeLiDos field study, personal light exposure data were collected in 9 sites, 7 countries, and 196 participants following the Guidolin et al. (2024) <doi:10.1186/s12889-024-20206-4> protocol. Data originate from wearable devices collecting personal light exposure at the eye level, chest, and the wrist. Questionnaires were collected via 'REDCap' and contain demographic information as well as chronotype, current conditions, sleep diaries, wear logs, and many more. This package makes loading the data from the respective repositories (<https://github.com/MeLiDosProject>) into R a breeze. It further contains some quality of life functions for label handling and data from 'REDCap'.

Sends supported 'R' objects to the 'Mellio' web app and creates
    polished, editable statistical tables in 'R'. The 'mellio_open' interface
    handles common hypothesis tests, model objects, model comparisons,
    descriptive summaries, tabular data, plots, and image files. The
    'melliotab' interface formats data frames, model summaries, correlation
    matrices, and side-by-side comparison tables with APA-style numeric
    formatting, confidence intervals, table notes, and optional significance
    markers. Manual table helpers can copy or save 'melliotab' output as
    'HTML', 'LaTeX', or 'Markdown' when file-based handoff is needed.
    Payloads include package-version metadata to support reproducible
    reporting and software citation.

A wrapper for the French Institute of Statistics (Insee) API
    Melodi <https://catalogue-donnees.insee.fr>. Download, search, filter data 
    and metadata from open data statistical public datasets, 
    in french or in english.

Performs multiple empirical likelihood tests. It offers an
    easy-to-use interface and flexibility in specifying hypotheses and
    calibration methods, extending the framework to simultaneous
    inferences.  The core computational routines are implemented using the
    'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for
    parallel computation.  Details of the testing procedures are provided
    in Kim, MacEachern, and Peruggia (2023)
    <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim,
    MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is
    available for further information. This work was supported by the U.S.
    National Science Foundation under Grants No. SES-1921523 and
    DMS-2015552.