Additional Notes

To wrap up this tutorial series, here are some important technical details regarding dependency management and performance optimization in jamovi modules.

1. Automated Dependency Management

When you run jmvtools::install(), jamovi doesn’t just build your R code—it also identifies, downloads, and bundles every dependency listed in your DESCRIPTION file.

Why bundle dependencies?

Zero-Install for Users: Users don’t need to have R or any specific R packages installed on their system to use your module.
Reproducibility: Your analysis will produce the same results today as it will five years from now, regardless of changes to CRAN.

2. CRAN Snapshots

When jamovi builds modules for the library, it installs R package dependencies from a fixed CRAN snapshot rather than from live CRAN. This snapshot is taken at the start of each jamovi series (e.g. 2.6, 2.7) and remains frozen for the lifetime of that series.

Why use a snapshot?

This protects users from breaking changes in third-party R packages. If a package author releases an update that changes behaviour or removes a function, modules built against the snapshot continue to work exactly as intended.

The trade-off

Because the snapshot is frozen, the packages bundled with your module may lag slightly behind the latest versions available on CRAN. The snapshot is moved forward each time a new jamovi series is released — typically every 6–12 months.

Using a newer package version

If you genuinely need a package version that is newer than the current snapshot, you can add a Remotes: field to your DESCRIPTION file pointing to a specific GitHub release or commit:

Remotes: wviechtb/metafor#9b1c7081b6b451108670fea480e83d6cb49387e8

Warning

Use Remotes: sparingly and with care. Pinning a dependency to a version outside the snapshot can conflict with the same package used by other modules, potentially breaking analyses that users rely on.

3. Optimizing Performance with Namespaces

While it’s common in standard R packages to use import() in the NAMESPACE file, we recommend using the :: operator instead (e.g., stats::t.test).

Benefits of this approach:

Lazy Loading: Dependencies are only loaded into memory at the exact moment they are needed.
Reduced RAM: Only the necessary parts of a package are loaded, keeping jamovi’s memory footprint small.

3. Understanding Your Tools

Tool	Primary Role	Used For…
`jmvtools`	jamovi Bridge	Creating modules, adding analyses, and installing into the jamovi application.
`devtools`	R Development	Standard R package tasks like testing (`test()`) and installing into your R console.

Next Step: Your module is now ready for the world! Explore the Module Showcase to see what others have built.