Your Guide to Quality Data Science Products

Ensure quality and reliability in R Shiny apps with comprehensive testing. Learn about unit, server function, and snapshot-based tests using {testthat} and {shinytest2}.

Get to know encapsulated OOP in R: Let's explore Reference Classes and R6, their features, and how they stack up against functional OOP.

Discover how AI/ML are transforming drug design, drug development, and clinical trials, enhancing efficiency, safety, and patient outcomes in the pharmaceutical industry.

Discover how machine learning enhances RNA-ligand binding predictions and outperforms traditional methods through our collaboration with IIMCB.

{shiny.telemetry} 0.3.0 introduces enhanced Shiny app analytics with advanced user tracking, error monitoring, MongoDB support, and improved security.

Explore Rhino 1.9.0's enhanced formating tools and seamless {bslib} integration for superior flexibility and styling in your Shiny applications.

Learn how creating a universe of R packages like Elkem's elkemVerse can simplify development, enhance scalability, and improve integration.

Which is faster, option A or option B? R Microbenchmark takes all guesswork out of the equation by allowing you to compare runtime statistics of different R expressions.

Learn about ShinyProxy 3.1.1 enhancements, improving Shiny app scalability and efficiency.

The current state of data science IDEs leaves a lot to be desired. Can a new offering from Posit - Positron IDE - be a go-to solution for R and Python professionals?

Read our complete guide to time series forecasting in R and learn how to train and evaluate time series models on your datasets.

Learn how the admiral package can improve clinical submissions. Explore features, goals, and contribution opportunities.