Another 3 months exact from our previous release, we are very proud to announce the release of version 1.1.2 of the Luminescence R package.

This release comes with a many (mostly rare) crashes being fixed, a number of cosmetic improvements to the plots, and a massive speed boost in the analyse_IRSAR.RF() function. Behind the scenes, during these past months we have introduced some improvements to our test infrastructure and continued our process of deprecation of redundant functions. In total we addressed 109 issues in 523 commits.

We have moved this website under our new subdomain at the Institute of Geography of the University of Heidelberg. From now you will find us at this URL:

https://replay.geog.uni-heidelberg.de/

The old links will continue to work.

Exactly 3 months from our previous release, we are excited to announce the release of version 1.1.1 of the Luminescence R package.

We were initially wondering whether this should be a minor or a major release, as we put a lot of effort in to make the package more solid and refined; we decided to call it a minor release just because there are not many new features in this version. However, there are some important regression fixes, many graphical improvements and also some performance speed-ups. In total we addressed 69 issues in 371 commits.

One of the challenges I faced as I started working on the Luminescence R package was how to navigate the very large set of functions that the package provides (by the last count, there are 155 functions that are exposed to the user, plus several internal helpers).

As I fumbled my way through it, I started noticing that when a function failed on a certain input, similar failures would also occur elsewhere. However, finding manually which other functions were affected was frustrating and time-consuming. Wouldn’t it be great if we could (quickly and with limited manual intervention) go through all the functions in the package and being told which are the ones that need to be fixed?

This got me to think about fuzz testing, and how that approach could be adapted for my purposes.

Much of the work that took place during the Luminescence 1.1.0 timeframe was devoted to adding new features to RLumShiny, the graphical interface to Luminescence built on the Shiny framework. We see this package as a good entry point to become acquainted with some of the functions provided by Luminescence and their various options.

Our work for the RLumShiny 0.2.5 release concentrated on adding a bunch of new apps:

• aliquotsize to either estimate the number of grains on an aliquot or to compute the packing density (based on calc_AliquotSize());

Only about 3 months from our previous release, we are happy to announce the release of version 1.1.0 of Luminescence R package. This is a major release, which comes with a few new features and many improvements, especially on the graphical side. In total we addressed 99 issues in 530 commits.

Many of the changes came from our work on the RLumShiny package, which made us consider in much greater detail some of the Luminescence functions. We will shortly bring what we think are exciting news on the RLumShiny side, but for the moment, let’s concentrate on what happened during this release cycle.

One of the hard lessons you have to learn as a package developer is that users usually do not care much about what you may have had in mind when writing a function. When we introduced the RLum-class object structure in 'Luminescence' in version 0.4.0 in December 2014 (more than ten years ago!), we had little doubt that it would be highly welcomed by users, as it streamlines their analysis pipelines. As a matter of fact, little is happening today in the package without this object structure, at least from a developer’s perspective.

One of the goals of the REPLAY project is to facilitate interfacing with the Luminescence package. This is a rather ambitious goal, but luckily there is already something available to make the use of Luminescence more direct and approachable.

The RLumShiny package integrates a bunch of Luminescence functions into a web-based app built on top of the shiny framework. This allows the users to work interactively with their data, letting them explore the available options and see how they affect the plots and results. This provides an easy entry point to discover what the package provides while performing an analysis of interest, or perhaps just playing around with sample data.

With the release of version 1.0 still very fresh, we are announcing the release of version 1.0.1, the first maintenance release of the Luminescence R package. This is a minor release meant to address one important regression, but it also brings a few additional robustness and correctness improvements. In total we addressed 13 issues in 53 commits.

We are happy to announce that the Luminescence R package has finally reached version 1.0.0. This is an important milestone, not only in the history of the Luminescence package, but also for the REPLAY project.

This makes it the third release since the start of the REPLAY project:

• Version 0.9.25 was released in September 2024, just over a month into the project, and it already contained a large number of changes (422 commits)

• Version 0.9.26 was a minor release (73 commits) to fix a couple of small regressions that we didn’t want to affect our first REPLAY webinar

• Version 1.0.0 contains most of the work that has occurred between September and February (886 commits) in a number of key areas, which we’ll outline below.