Agreement between two sets of continuous random variables of log-normal distribution I have two sets of measurements from two different proxies (for simplicity we can assume one to be the gold standard).  I want to report a number representing their agreement, ideally with 1.0 representing perfect agreement.  They are each continuous variables in the same units having log-normal distribution.
I have used cor() in R, but this doesn't take into account bias, e.g. cor(x,x/2-1000) = cor(x,x) = 1.  RMSE doesn't convey an intuitive sense of agreement or disagreement.
Lin's concordance seems like a good choice, but I am unsure how to use it properly.  There are several CCC packages for R with many different options.
 A: Lin's concordance correlation is an appropriate single measure of agreement. Naturally it will not, and cannot, tell you much about the fine structure of agreement and disagreement. 
With data that are (roughly or exactly) lognormal, the same question arises as with correlation, whether comparison on logarithmic scale makes as much or more sense than comparison on the original scale. 
I can't help with your difficulties in deciding how to do it in R, which are not stated specifically and arguably are off-topic here in any case. If you could post sample data, alternative solutions could be explored. 
Relevant threads here include 
Does concordance correlation require data to be normally distributed?
Does the concordance correlation coefficient make linearity or monotone assumptions?
A: Use McNemar's test to evaluate marginal homogeneity.
Use the poly/tetra-choric correlation coefficient if its assumptions are sufficiently plausible.
Possibly test association between proxies with the log odds ratio.
For more agreement tests with continuous variables check this out: 
http://web1.sph.emory.edu/observeragreement/review_manuscript.pdf

To assess raw agreement you can utilize Cohen's kappa for categorical data: 


*

*from its p-value, establish that agreement exceeds that expected under the null hypothesis of random ratings; 

*interpret the magnitude of kappa as an intraclass correlation. 


In R you can use the psych package (cohen.kappa function).
Alternatively, calculate the intraclass correlation directly instead of a kappa statistic.
