Inspire by Patrick's recent presentation I thought I would look at the transition probabilities of End--End pairs. These are the likelihoods (as a fraction of 1) that a word with a given ending will be followed by a word with another given ending. (I'm surprised nobody has done this before, so if they have, please do say.)
All numbers are from a selection of running text in Currier B. Only those word endings which occur at least 250 times have been counted, and results only show relationships at 0.05 or higher. Also, [in] was processed as a single glyph. The total likelihood for all the results given is in parentheses at the end.
[d]: [y] .46, [in] .17, [l] .14, [r] .12 (.89)
[l]: [y] .47, [in] .15, [l] .15, [r] .13 (.90)
[m]: [y] .31, [in] .25, [r] .14, [l] .13, [o] .05 (.88)
[in]: [y] .46, [l] .16, [in] .15, [r] .12 (.89)
[o]: [y] .32, [in] .19, [l] .18, [r] .17, [o] .05 (.91)
[r]: [y] .39, [in] .18, [r] .17, [l] .14 (.88)
[s]: [in] .33, [y] .27, [r] .16, [l] .13, [s] .06 (.95)
[y]: [y] .49, [in] .17, [l] .14, [r] .12 (.92)
My initial thoughts are that [d, l, in, y] are all very similar. [r] is a bit lower on [y] but not hugely different. But [m, o, s] are all quite variant. These are quite low counts (along with [d]), so it could be that there's simply a lot of spikiness in the data. Hard to tell

.
Breaking the data down by bigrams for the first feature shows no big difference: [ol] and [al] are similar to [l], [or] and [ar] are similar to [r], [ain] and [iin] are similar to [in]
But the differences between [ey] and [edy] are worth breaking down, both as the first and second feature. Each occurs thousands of times: about 2,300 and 3,500, respectively. [$y] stands for some other word ending in [y], including [dy] not preceded by [e].
[edy]: [edy] .29, [in] .14, [$y] .14, [l] .13, [ey] .11, [r] .10
[ey]: [in] .20, [ey] .19, [edy] .15, [l] .14, [r] .12, [$y] .12
[d]: [edy] .21, [in] .17, [$y] .15, [l] .14, [r] .12, [ey] .10
[l]: [edy] .23, [in] .15, [l] .15, [$y] .14, [r] .13, [ey] .10
[m]: [in] .25, [r] .14, [l] .13, [edy] .11, [ey] .10, [$y] .10, [o] .05
[in]: [$y] .19, [l] .16, [in] .15, [edy] .14, [ey] .13, [r] .12
[o]: [in] .19, [l] .18, [r] .17, [edy] .12, [$y] .11, [ey] .09, [o] .05
[r]: [in] .18, [r] .17, [$y] .16, [l] .14, [edy] .13, [ey] .10
[s]: [in] .33, [r] .16, [l] .13, [$y] .11, [edy] .09, [ey] .07, [s] .06
This data looks a bit messy, but a few things can be seen:
- [edy] clearly likes to cluster --- I think we already knew this.
- [l] and [d] have a higher preference for [edy] than others.
- [ey] also has a high preference for itself
- [in] and [m] also have a preference for [ey] over [edy] (taking into account the number of tokens)
- [in] (however) clearly has a preference for words ending [y] which are neither [edy] or [ey] (apparently [ky, ckhy] are big chunk of the difference)