Implements user interfaces for log-linear models, Bayesian model averaging and 
    Bayesian Dirichlet process mixture models. See McIntyre, Fellows, Gutreuter and Hladik (2022) <doi:10.2196/32645>.

This gadget allows you to use the 'recipes' package belonging to 'tidymodels' to carry 
    out the data preprocessing tasks in an interactive way. Build your 'recipe' by dragging the variables, 
    visually analyze your data to decide which steps to use, add those steps and preprocess your data.

Render R Markdown reports to HTML and display them instantly in a new browser tab from within a 'shiny' application. Replaces file downloads with a seamless in-browser approach.

Provides functionality to extract reactive expressions from a 'shiny' application and 
    convert them into stand-alone R scripts. This enables users to reproduce tables and visualisations 
    outside the interactive UI, facilitating integration into static reports or automated workflows 
    without requiring access to the original application source code.

Shiny wrappers for the RGL package. This package exposes RGL's
    ability to export WebGL visualization in a shiny-friendly format.

A 'shiny' interface for a simpler use of the 'sbm' R package.
    It also contains useful functions to easily explore the 'sbm' package
    results.  With this package you should be able to use the stochastic
    block model without any knowledge in R, get automatic reports and nice
    visuals, as well as learning the basic functions of 'sbm'.

Create a skeleton 'shiny' application with create_template() that is
    reproducible, can be saved and meets academic standards for attribution.
    Forked from 'wallace'. Code is split into modules that are loaded and linked
    together automatically and each call one function. Guidance pages explain
    modules to users and flexible logging informs them of any errors. Options
    enable asynchronous operations, viewing of source code, interactive maps and
    data tables. Use to create complex analytical applications, following best
    practices in open science and software development. Includes functions for
    automating repetitive development tasks and an example application at
    run_shinyscholar() that requires
    install.packages("shinyscholar", dependencies = TRUE). A guide to developing
    applications can be found on the package website.