CRAN release ‘Luminescence’ v0.9.24

- waiting for the summer cat -

by RLum.Network (June 7, 2024)

Today, we released 0.9.24 of 'Luminescence' on CRAN. This release was boiling for quite a while and brings substantial improvements on top of the usual bug fixes.

Before updating, please note that this new version requires at least R 4.3!

Highlights

Channel trimming of curves with trim_RLum.Data()

Sequences are only sometimes consistent when faced with different datasets measured over different machines over a long time. For instance, you may have modified the recorded number of channels for the shine-down curve. If such datasets are merged, most of the analysis functions in 'Luminescence' will rightfully flag an error and refuse to analyse the data. But even without such an error, it is sometimes meaningful to trim the number of channels and here trim_RLum.Data() is your friend:

par(mfrow = c(1,2))
plot_RLum(
  object = TL_curve, 
  par.local = FALSE,
  mtext = "untouched")
plot_RLum(
  object = trim_RLum.Data(TL_curve, trim_range = 300), 
  par.local = FALSE, 
  mtext = "trimmed")

The function works also on RLum.Analysis objects and lists. If no trim_range is provided, the function trims all objects to a similar maximum length. On top, the function analyse_SAR.CWOSL() gained a new argument trim_channels that does this for any dataset (only the OSL/IRSL curves) you want to analyse automatically.

One single import function is all you need import_Data()

If you have ever used an R script for different measurement data, for instance from a Risø or a Freiberg machine, it was somewhat annoying to always being force to modify the function that imports the data: import_Data() removes that issue. Just pass any file, and the function will try all available import functions (commencing with read_) to import your data. If nothing works, you can be sure that "Luminescence" does not know what to do with your data.

All news can be found on GitHub

Stay safe!

Your R Luminescence Team