Evaluates R source codes by variable and/or functions names. Similar source codes should deliver similarity coefficients near one. Since neither the frequency nor the order of the used names is considered, a manual inspection of the R source code is required to check for similarity. Possible use cases include detection of code clones for improving
    software quality and of plagiarism amongst students' assignments.

Scelestial infers a lineage tree from single-cell DNA mutation matrix. 
	It generates a tree with approximately maximum parsimony through 
	a Steiner tree approximation algorithm.

Client for Rserve, allowing to connect to Rserve instances and issue commands.

Provides a tool for mass deployment of shiny apps to 'RStudio Connect' or 'Shiny Server'. Multiple user accounts and servers can be configured for deployment.

Programmatic deployment interface for 'RPubs',
    'shinyapps.io', and 'Posit Connect'. Supported content types include R
    Markdown documents, Shiny applications, Plumber APIs, plots, and
    static web content.

Provides a generic implementation of the 'RStudio' connection contract to 
    make it easier for database connections, and other type of connections, opened 
    via R packages integrate with the connections pane inside the 'RStudio' interactive
    development environment (IDE).

Uses Elsevier 'Scopus' API
    <https://dev.elsevier.com/sc_apis.html> to download 
    information about authors and their citations.

A method to download Department of Education College
     Scorecard data using the public API
     <https://collegescorecard.ed.gov/data/data-documentation/>. It is based on
     the 'dplyr' model of piped commands to select and filter data in a
     single chained function call.  An API key from the U.S. Department of
     Education is required.

The Stochastic Dominance (SD) is the classical way of comparing two
    random prospects, using their distribution functions. Almost Stochastic
    Dominance (ASD) has also been developed to cover the SD failures due to 
    the extreme utility functions. This package focuses on classical and heuristic methods
    for testing the first and second SD and ASD methods given the probability mass
    function (PMF) of the random prospects. The goal is to apply these methods
    easily, efficiently, and effectively on real-world datasets. For more
    details see Hanoch and Levy (1969) <doi:10.2307/2296431>, Leshno and Levy
    (2002) <doi:10.1287/mnsc.48.8.1074.169>, and Tzeng et al. (2012)
    <doi:10.1287/mnsc.1120.1616>.