Thanks for this useful application that can help to analyze Raw data files, and gain a better understanding of what's going on under-the-hood.
If I may, there is one useful (optional) statistical metric that could be a useful addition for geeks.
When a shot is made of a featureless uniformly lit surface, the subtraction of the usually 2 Green filtered sensel responses per [R,G1,G2,B] set can provide useful info. If the subject and lighting is uniform, then the two green sensels should give the same output (except for read noise). If they don't, and it is measured over a larger area, a calibration bias may be detected (their average value is different). When their per channel standard deviation noise is subtracted in quadrature, read noise can be pretty accurately determined in a single shot. The subtraction will eliminate pattern noise because it is not random, and only shot-noise will remain. It can also detect outliers as hot or dead Green filtered sensels.
It could also be used to indirectly improve uniform lighting of a scene by minimizing the difference between the 2 green channel averages, but other channels could also be used for that.
Comparing spatial differences between sensels within a channel could also indicate a measure of focus-quality on highly detailed subjects or focus bracketing sequences.
One could also think about implementing a noise spectrum measurement, for the detection of on-sensor, or in-ADC pipeline, noise reduction.
Also estimated White-balance Raw channel multipliers would be useful.
Just some thoughts for when you run out of ideas
Thanks again for making RawDigger available.