Search Results for "edex-ui"

The advanced-shiny repository is a curated collection of practical tips, design patterns, and mini Shiny apps focused on solving real-world challenges in R Shiny applications. The author (Dean Attali) collected many of the “harder” or less-documented tricks he uses or encounters frequently—things like controlling UI behavior dynamically, managing reactive logic, optimizing interactivity, and structuring large Shiny codebases. The repo’s structure includes folders of example apps each implementing a specific trick or pattern (e.g. loading spinners, dynamic UI, hiding/showing UI elements, handling file uploads, URL parameter inputs). Each example is runnable so developers can inspect code and behavior side-by-side. ...

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.

...It supports custom templates, themes, and configuration. pkgdown 2.0.0 includes an upgrade from Bootstrap 3 to Bootstrap 5, which is accompanied by a whole bunch of minor UI improvements. If you’ve heavily customised your site, there’s a small chance that this will break your site, so everyone needs to explicitly opt in to the upgrade.

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.

RStan is the R interface to Stan, a C++ library for statistical modeling and high-performance statistical computation. It lets users specify models in the Stan modeling language (for Bayesian inference), compile them, and perform inference from R. Key inference approaches include full Bayesian inference via Hamiltonian Monte Carlo (specifically the No-U-Turn Sampler, NUTS), approximate Bayesian inference via variational methods, and optimization (penalized likelihood). RStan integrates with...