A set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created 'rigr' to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. 'rigr' output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.

Functionality to download stock prices,
    cryptocurrency data, and more from the 'Tiingo' API
    <https://api.tiingo.com/>.

Tools to download data from the ilostat database
    <https://ilostat.ilo.org> together with search and
    manipulation utilities.

An interface to the powerful and fairly complete computer algebra system 'Maxima'.
    It can be used to start and control 'Maxima' from within R by entering 'Maxima' commands. 
    Results from 'Maxima' can be parsed and evaluated in R. 
    It facilitates outputting results from 'Maxima' in 'LaTeX' and 'MathML'. 
    2D and 3D plots can be displayed directly. 
    This package also registers a 'knitr'-engine enabling 'Maxima' code chunks 
    to be written in 'RMarkdown' documents.

Provides functions to read 'ImageJ' (<http://imagej.nih.gov/ij/>)
    Region of Interest (ROI) files, to plot the ROIs and to convert them to
    'spatstat' (<http://spatstat.org/>) spatial patterns.

A pure R implementation of the median cut algorithm.
    Extracts the dominant colors from an image, and turns them into
    a scale for use in plots or for fun!

The metrics() function calculates measures of scholarly impact. 
    These include conventional measures, such as the number of publications
    and the total citations to all publications, as well as modern and 
    robust metrics based on the vector of citations associated with each 
    publication, such as the h index and many of its variants or rivals.
    These methods are described in Ruscio et al. (2012) 
    <DOI: 10.1080/15366367.2012.711147>.