Open questions on the multivariable normal's code
add sd vector and merged all reps into one
Got the tabling function done and tested.
high sd for algorithm make sense as it has both a high ratio to vary from and it has a lot of points with low n throwing it off.

naive doing better than snappy2 though seems wierd.
found and fixed some bugs.
Made the multivariableRegression script return some values so they can be looked at afterwords.
Found a time-sink; marked it with a TODO
Merge branch 'master' of https://github.com/Daniel306/stat440
Make speed tests easier to debug
fixed test.speed and test.speed.main as discusses in chat.
Commit some debugging
Merge branch 'master' of https://github.com/Daniel306/stat440

Conflicts:
	R/test.constants.R
	R/test.speed.R
	R/test.speed.main.R
cumvar(), which computes cumulative variances (of vectors only!!)
test rearchitecture to support analytic moment formulas
[UNTESTED] import the marginal variance formulas
hacks to speed up test.correctness.R dev cycle
omg i broke everything in the (second) last commit
start to rewrite the moment code: cummean.marginals and cumvar.marginals
yakshaving: marginals_do, which has the magic (which turned out to be do.call) I was looking for years ago.
yakshaving: idx2title
yakshaving: reasonable_subset
[BROKEN] large refactor to expressing convergence plots much more elegant

Someone on #R helped me figure out how to do S[*idx], which was the breakthrough
that allowed me to trim and shorten a lot.
Also I decided to toss out trying to be speed-efficient in favour of being expressivity-efficient:
 good bye cumvar and cummean (((they're still around, but I'm not using them)))
This is all in support of making expressing analytic