Interactive Reingold-Tilford tree diagrams created using 'D3.js', where every node can be expanded and collapsed by clicking on it.
    Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame.
    See 'collapsibleTree' website for more information and examples.

Map functions while capturing results, errors, warnings, messages and other output tidily, then filter and summarise data frames or lists on the basis of those side effects.

Provides the basic functionality to interact with the Collatz conjecture.
    The parameterisation uses the same (P,a,b) notation as Conway's generalisations.
    Besides the function and reverse function, there is also functionality to retrieve
    the hailstone sequence, the "stopping time"/"total stopping time", or tree-graph. 
    The only restriction placed on parameters is that both P and a can't be 0.
    For further reading, see <https://en.wikipedia.org/wiki/Collatz_conjecture>.

Provides high performance container data types such
    as queues, stacks, deques, dicts and ordered dicts. Benchmarks
    <https://randy3k.github.io/collections/articles/benchmark.html> have
    shown that these containers are asymptotically more efficient than
    those offered by other packages.

Provides a tidied subset of the US College Scorecard dataset,
    containing institutional characteristics, enrollment, student aid,
    costs, and student outcomes at institutions of higher education in the
    United States.

Datasets for the book entitled "Modelling Survival Data in Medical Research" by Collett (2023) <doi:10.1201/9781003282525>. The datasets provide extensive examples of time-to-event data.

Built upon popular R packages such as 'ggstatsplot' and 'ARTool', this collection offers a wide array of tools for simplifying reproducible analyses, generating high-quality visualizations, and producing 'APA'-compliant outputs. The primary goal of this package is to significantly reduce repetitive coding efforts, allowing you to focus on interpreting results. Whether you're dealing with ANOVA assumptions, reporting effect sizes, or creating publication-ready visualizations, this package makes these tasks easier.