It is not RAD, it's just a more accurate way to measure reach. The RAD method sounds sensible, but it's nonsense. Well-intended nonsense, but nonsense. For example, RAD doesn't account for seat-tube angle, so if you optimize a bike for winch-and-plummet riding with a super steep seat-tube angle, RAD produces an extremely short butt-to-bar distance. Similarly, optimize a hardtail for milder terrain and you'll want a more traditional seat-tube angle, but RAD does not account for this. RAD doesn't even separate reach from stack, so it equally recommends a long bike with time-trial bar height as a vertical bike with zero reach.
The evolution of RAD, RAAD, incorporates an angle to try to address some of these flaws, but it's still a workaround full of recommended constants necessary to produce acceptable results.
A proper predictive method incorporates many physiological measurements and allows variability for rider preference and terrain. As with my normalized reach, such a method adds complexity, but it's better to be complex and correct than simple and wrong.
The other approach is via data-driven observation: gather a lot of data on riders and how they like their bikes to fit, then plot the relationships to each variable. It can be even simpler by looking at manufacturers' geometry charts and plotting correlations between recommended rider heights and recommended frame geometries. It's a simplistic method that doesn't attempt to explain the underlying reasons for various dimensions, but, given enough data and enough time, the numbers will stumble in the right direction until nearly optimized. After more than a century of bike design - and nearly forty years of mountain-specific design - I believe we've reached a point where the data-driven approach is close enough for most riders.