An effortless 'ndjson' (newline-delimited 'JSON') logger, with two primary
    log-writing interfaces. It provides a set of wrappings for base R's
    message(), warning(), and stop() functions that maintain identical
    functionality, but also log the handler message to an 'ndjson' log file.
    'loggit' also exports its internal 'loggit()' function for powerful and
    configurable custom logging. No change in existing code is necessary to use
    this package, and should only require additions to fully leverage the power
    of the logging system. 'loggit' also provides a log reader for reading an
    'ndjson' log file into a data frame, log rotation, and live echo of the
    'ndjson' log messages to terminal 'stdout' for log capture by external
    systems (like containers). 'loggit' is ideal for Shiny apps, data pipelines,
    modeling work flows, and more. Please see the vignettes for detailed example
    use cases.

An easy-to-use 'ndjson' (newline-delimited 'JSON') logger.
    It provides a set of wrappings for base R's message(), warning(), and
    stop() functions that maintain identical functionality, but also log
    the handler message to an 'ndjson' log file.
    No change in existing code is necessary to use this package, and should
    only require additions to fully leverage the power of the logging system.

Implementation of the Swiss Confederation's standard analysis model for salary analyses <https://www.ebg.admin.ch/dam/ebg/en/dokumente/lohngleichheit/infos-zu-analysen/standard-analysemodellzurueberpruefungderlohngleichheitzwischenf.pdf.download.pdf/methodological_approachformonitoringcompliancewithwageequalitybe.pdf> in R. The analysis is run at company-level and the model is intended for companies with 50 or more employees (apprentices, trainees/interns and expats are not included in the analysis). Employees with at least 100 employees are required by the Gender Equality Act to conduct an equal pay analysis.
    This package allows users to run the equal salary analysis in R, providing additional transparency with respect to the methodology and simple automation possibilities.