drawing from distribution of fixed effects

[RobertClay]

by definition regression coefficients are multivariate normal distributed as

beta_hat ~ N_p(beta, (X^TX)^{-1}sigma^2)

We want to draw random coefficients to induce uncertainty in model coefficient values

• [x] calculate the covariance matrix (X^TX)^{-1}sigma^2. already calculated for most models using vcov() command. check for clm etc.
• [x] put in the pipeline somewhere that model fixed effects coefficients are nudged by a random draw from the N_p(0, (X^TX)^{-1}sigma^2). maybe call a function in presetup or as soon as model is called. may be simpler to just add this in every time step so we're using rpy2 as little as possible.
• [x] test that this doesn't produce stupid results.

This whole approach may be done much better using Gibbs sampling I think. check if theres an R package to do this for regression. start simple for now.

[RobertClay]

this has already been pulled somewhere...

• [ ] delete the old branch
• [ ] make sure all modules have this enabled.
• [ ] push to dev.