What is B. D. Ripley's method of seeding the Mersenne-Twister RNG? R's documentation behind ?runif states that the default RNG is

"Mersenne-Twister": From Matsumoto and Nishimura (1998); code updated
in 2002. A twisted GFSR with period 2^19937 - 1 and equidistribution
in 623 consecutive dimensions (over the whole period). The ‘seed’ is a
624-dimensional set of 32-bit integers plus a current position in that
set.
R uses its own initialization method due to B. D. Ripley and is not
affected by the initialization issue in the 1998 code of Matsumoto and
Nishimura addressed in a 2002 update.

I would like to read about R's initialization method due to B. D. Ripley but there is no reference to a particular paper or chapter in a book. I would appreciate someone describing - or pointing me to a reference that would describe - B. D. Ripley's method of seeding the Mersenne-Twister RNG.
link to source code here
 A: I don't know if it's documented except in the code (src/main/RNG.c)
static void RNG_Init(RNGtype kind, Int32 seed)
{
    int j;

    BM_norm_keep = 0.0; /* zap Box-Muller history */

    /* Initial scrambling */
    for (j = 0; j < 50; j++)
        seed = (69069 * seed + 1);
    switch (kind) {
    case WICHMANN_HILL:
    case MARSAGLIA_MULTICARRY:
    case SUPER_DUPER:
    case MERSENNE_TWISTER:
    /* i_seed[0] is mti, *but* this is needed for historical consistency */
    for (j = 0; j < RNG_Table[kind].n_seed; j++) {
        seed = (69069 * seed + 1);
        RNG_Table[kind].i_seed[j] = seed;
    }
    FixupSeeds(kind, 1);
    /* snippety snip */
}

That is, the initialisation works by running a linear congruential generator $x\mapsto 69069\cdot x+1$ for 50 iterations, then enough iterations to fill up the seed, then imposing any constraints needed by the specific generator.  This makes sure any user-supplied seeds (often small integers) end up spread across the seed space of the generator.
This congruential generator is due to Marsaglia, and Prof Ripley used and recommended it as a pseudorandom number source back in the days when the demand for random numbers was low enough for its period to be sufficient.
