I was reading Rao's chapter 4 of his Linear Statistical Inference, 2nd ed. He uses the notation $R(G:X)$ in section 4.i (p. 294, formula (e), p. 296 formula (4i.1.21)) and that notation appears again in the last paragraph of p. 300. Unfortunately, Rao does not define the notation and I could not quite point out the meaning from the context. Does anyone know what it means?
Another book uses something similar $S(X:V)$ and they also do not define the notation, and again I am not quite sure what it means. (To be more explicit, they define $S$ to mean the image space or column space, and my best guess is that $X:V$ simply means the concatenation $[X, V]$ of the two matrices. Both expressions $X:V$ and $G:X$ appear in the context of the General Gauss Markov Model.)
I know this is a long shot, but if you know, I'd appreaciate if you let me know.