R Frameworks for ChromeOS

Browse free open source R Frameworks for ChromeOS and projects below. Use the toggles on the left to filter open source R Frameworks for ChromeOS by OS, license, language, programming language, and project status.

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  • 1
    golem

    golem

    A Framework for Building Robust Shiny Apps

    golem is an opinionated framework for developing production-grade Shiny applications in R, treating the app like a full R package. It scaffolds project structure, testing, documentation, CI/CD, and supports containerization—streamlining the build-to-deploy pipeline while enforcing clean architecture and maintainability.
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    Shiny

    Shiny

    Build interactive web apps directly from R with Shiny framework

    Shiny is an R package from RStudio that enables users to build interactive web applications using R without requiring knowledge of JavaScript, HTML, or CSS. It allows statisticians and data scientists to turn their analyses into fully functional web dashboards with reactive elements, data inputs, visualizations, and controls, making data communication more effective and dynamic.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    easystats

    easystats

    The R easystats-project

    easystats is a meta‑package that installs and unifies a suite of R packages for post‑processing statistical models. It delivers a consistent API to assess model performance, effect sizes, parameters, and to generate reports and visualizations, all with minimal dependencies and maximum clarity.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 4
    future

    future

    R package: future: Unified Parallel and Distributed Processing in R

    The future package in R provides a unified abstraction for asynchronous and/or parallel computation. It allows R expressions to be scheduled for future evaluation, with the result retrieved later, in a way decoupled from the specific backend used. This lets code be written in a way that works with sequential execution, multicore, multisession, cluster, or remote compute backends, without changing the high-level code. It handles automatic exporting of needed global variables/functions, managing of packages, RNG, etc. Automatic detection and export of global objects and functions needed by future expressions, so the user doesn’t need to manage that manually. Ability to control how futures are resolved.
    Downloads: 0 This Week
    Last Update:
    See Project
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