I am not an expert in mixed effects models, though I have used the older lme() function in the nlme R package for various analyses.
That being said, three comments:
I would not collapse multiple measurements per tool per subject into a single aggregate measure. You lose the per subject assessment of variability that you want to know about for each of the two tools being used. That is presumably why you are taking multiple measurements, right? Not only is there a presumptive difference between the two tools that may be relevant, but the within subject variability may be quite different between the two tools as well.
Since you are going to use R, and software specific questions tend to be off-topic here, I would recommend that you post your query to the r-sig-mixed-models group, which is a focused audience in this domain:
- I would disagree with your assessment of lmer() not being able to handle clustered measurements on the same subject as a random effect. This can be done with the older lme() function, and lmer() provides far more functionality. The particular specification for that type of model in lmer() is something that the above list folks can provide to you, based upon further interaction on the list.
Lastly, if you are looking to assess the agreement between the two tools, and not just whether the mean measurements are different, you may want to look at the Bland-Altman approaches as well:
including this paper, which is linked on their site: