Like JohnV I was interested in the Hansen reverse-engineering project and thought that this area (software development) might be where I could contribute. In this vein I have the following questions:

1) Is there work than can be done by the non-statistician to enable the statistician to focus more on the design and interpretation of the analysis. If there is this type of work, and there is a will to support it, then software “friendlier” than R may have advantages.

2) Are there tasks that occupy the time of principal researchers where purpose built, or integrated software can play a significant role? For example I have seen many requests for data cut this way and that, data sets to be correlated, etc.

3) JohnV has certainly demonstrated that there is sufficient good will and expertise that can be harnessed towards these ends.

c

Finally, if you want to develop something like OpenTemp, you can write it up and test it as an R function, and its principles, strengths, and weaknesses will be totally transparent

Please explain how this is different than other code?

recon.crn12=ushcn_wt(index_good,ushcn=ushcn.tobs)
ts.plot(recon.crn12)

I overlaid a five-year trailing average and a linear trend on the complete data set. Unforunately, I could not easily do a year-by-year difference because my digitized years did not always line up with the actual years. (Misalignment is visible in the graph above).

I’ve always wondered what the trend line in such diagrams is supposed to mean. What phsyical interpretation should it be given? My own prejudice is that a trend line is useless. It presupposes a linear trend without justification.

I did some work with rural CRN12 vs rural CRN123 here:

http://www.climateaudit.org/?p=2124#comment-147568

The coverage is much better when the CRN3 stations are included.