TimeFRAME is an R-package that estimates process rates based on isotopic measurements using Bayesian modelling. The more complex methods implemented in the package consider temporal correlations between sampling times, which gives the methods an edge over the FRAME package, where each datapoint is considered independently. While TimeFRAME was developed mainly to analyse N2O isotopic data, it can also be used for analysis of other isotopic datasets.

There is substantial testing of the methods in the manuscript, with respect to uncertainty in input parameters, and model complexity. Comparison with other approaches is also done. This gives credibility to the functions implemented, in the sense that they may  be used for analysis of similar datasets. Also, there is ample discussion of caution that should be exerted in model choice and configurations, so users will find much guidance in the technical note.

My only remark is that the methods are purely statistical approaches. Timeseries data can also be tackled with dynamic, mechanistic approaches (based on differential equations), which typically have few parameters to be fitted and can ingest more diverse data sets, i.e. including concentrations. While I am not claiming that the authors should also discuss those methods at length, it would be desirable that they at least mention this -alternative- approach to stable isotopic data analysis for timeseries data.