shinytest2 0.5.0 is Now Available on CRAN I'm excited to announce the release of shinytest2 0.5.0, bringing significant improvements to the R package testing experience for Shiny applications! 🚀 Key Features: Local Package Development Support One of the most requested features is here! `AppDriver$new(app_dir=)` now supports local package development. As long as you have `library()` or `require()` call in your `app.R`, `{shinytest2}` automatically executes `pkgload::load_all()` to load your local R package's source code. This means you no longer need to install your package before testing—a huge time-saver during development! Function Execution Support You can now pass functions directly to `AppDriver$new(app_dir=)`. This provides flexibility for testing apps with dynamic configurations or custom execution environments, such as Shiny packages made using `{golem}`! The function runs in a background R process, similar to `{mirai}`, with full control over the testing environment. Enhanced Package Testing Workflow This release introduces `local_app_support()` and `with_app_support()`, providing package authors with a flexible way to scope app support files within tests. Package authors can now test Shiny applications directly within their package's existing`{testthat}` tests removing the need for calling `test_app()`. This will collect all snapshots in a single location using the package's existing testing infrastructure. Improved Test Recording `record_test()` gained a `record_in_package` parameter that intelligently saves test files to the appropriate location—either in the package's `tests/testthat/` directory or relative to the app's test directory. When recording tests for apps inside packages, `local_app_support()` is automatically added to the testing code. Modernized HTTP Stack Migrated from `{httr}` to `{httr2}` for all HTTP requests, ensuring compatibility with modern R web tooling and better long-term maintenance. 📚 Learn More: - Package website: https://lnkd.in/dt9ZtezY - Use-package vignette: https://lnkd.in/dvv7VRXj - GitHub: https://lnkd.in/dF6rF3CB #rstats #rshiny #DataScience #RStats #ShinyApp #RPackageDevelopment #SoftwareTesting

Thanks @sj-io for sharing this with me! I think the R package development skill is a bit too "high-level" for my tastes. I'd love to see a more "hardcore" or barebones version. For me this would be...

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Performs Granger causality tests on pairs of time series to determine causal relationships. Uses Vector Autoregressive (VAR) models to test whether one time series helps predict another beyond what the series' own past values provide. Returns structured results including p-values, test statistics, and causality conclusions for both directions.

Amazon.com: Longitudinal Data Analysis Using R: 9798261825210: Cernat, Alexandru: Books