Sometimes it's useful to know some information about your user in a 'Shiny' app. The available
    information is: browser name (such as 'Chrome' or 'Safari') and version, device type 
    (mobile or desktop), operating system (such as 'Windows' or 'Mac' or 'Android') and version,
    and browser dimensions.

Adds easy access to additional Twitter Bootstrap components to Shiny.

Add indicators (spinner, progress bar, gif) in your 'shiny'
  applications to show the user that the server is busy. And other tools to let
  your users know something is happening (send notifications, reports, ...).

Provides a combined slider and numeric input for usage in a 'Shiny' app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).

Makes the 'React' library 'Chakra UI' usable in 'Shiny' apps. 'Chakra UI' components include alert dialogs, drawers (sliding panels), menus, modals, popovers, sliders, and more.

Provides a scrolling chat interface with multiline input,
    suitable for creating chatbot apps based on Large Language Models
    (LLMs). Designed to work particularly well with the 'ellmer' R package
    for calling LLMs.

Provides an easy-to-use module for adding a chat to a Shiny app. Allows
    users to send messages and view messages from other users. 
    Messages can be stored in a database or a .rds file.

A 'shiny' application estimating the operating characteristics of the Student's t-test by Student (1908) <doi:10.1093/biomet/6.1.1>, Welch's t-test by Welch (1947) <doi:10.1093/biomet/34.1-2.28>, and Wilcoxon test by Wilcoxon (1945) <doi:10.2307/3001968> in one-sample or two-sample cases, in settings defined by the user (conditional distribution, sample size per group, location parameter per group, nuisance parameter per group), using Monte Carlo simulations Malvin H. Kalos, Paula A. Whitlock (2008) <doi:10.1002/9783527626212>.

You can easily add advanced cohort-building component
    to your analytical dashboard or simple 'Shiny' app.
    Then you can instantly start building cohorts using multiple
    filters of different types, filtering datasets, and filtering steps.
    Filters can be complex and data-specific, and together
    with multiple filtering steps you can use complex filtering rules.
    The cohort-building sidebar panel allows you to easily
    work with filters, add and remove filtering steps.
    It helps you with handling missing values during filtering,
    and provides instant filtering feedback with filter feedback plots.
    The GUI panel is not only compatible with native shiny
    bookmarking, but also provides reproducible R code.