An implementation of Kaplan, Betancourt, Steorts (2022) <doi:10.1080/00031305.2022.2041482> that creates representative records for use in downstream tasks after entity resolution is performed. Multiple methods for creating the representative records (data sets) are provided.

Reproducible research tools automates the creation of an analysis directory structure and work flow. There are R markdown
  skeletons which encapsulate typical analytic work flow steps. Functions will create appropriate modules which may
  pass data from one step to another.

Convenience wrapper that uses the 'rmarkdown' package to
    render small snippets of code to target formats that include both code
    and output.  The goal is to encourage the sharing of small,
    reproducible, and runnable examples on code-oriented websites, such as
    <https://stackoverflow.com> and <https://github.com>, or in email. The
    user's clipboard is the default source of input code and the default
    target for rendered output. 'reprex' also extracts clean, runnable R
    code from various common formats, such as copy/paste from an R
    session.

Includes data analysis and meta-analysis functions (e.g., to 
    calculate effect sizes and 95% Confidence Intervals (CI) on Standardised 
    Effect Sizes (d) for AB/BA cross-over repeated-measures experimental 
    designs), data presentation functions (e.g., density curve overlaid on 
    histogram),and the data sets analyzed in different research papers in 
    software engineering (e.g., related to software defect prediction or multi-
    site experiment concerning the extent to which structured abstracts were
    clearer and more complete than conventional abstracts) to streamline
    reproducible research in software engineering.

A collection of high-level, machine- and OS-independent tools
    for making reproducible and reusable content in R.
    The two workhorse functions are 'Cache()' and 'prepInputs()'.
    'Cache()' allows for nested caching, is robust to environments and objects
    with environments (like functions), and deals with some classes of 
    file-backed R objects e.g., from 'terra' and 'raster' packages. 
    Both functions have been developed to be foundational components of data
    retrieval and processing in continuous workflow situations. In both functions,
    efforts are made to make the first and subsequent calls of functions have 
    the same result, but faster at subsequent times by way of checksums
    and digesting. Several features are still under development, including
    cloud storage of cached objects allowing for sharing between users. Several
    advanced options are available, see '?reproducibleOptions()'.