MCMCglmm error-in-variables (total least squares) model?

I posted this question on Stack Overflow a week ago but received no answers:

http://stackoverflow.com/questions/34446618/bayesian-error-in-variables-total-least-squares-model-in-r-using-mcmcglmm

This may be a more appropriate venue.


I am fitting some Bayesian linear mixed models using the MCMCglmm package.
My data includes predictors that are measured with error. I'd therefore
like to build a model that takes this into account. My understanding is
that a basic mixed effects model in MCMCglmm will minimize error only for
the response variable (as in frequentist OLS regression). In other words,
vertical errors will be minimized. Instead, I'd like to minimize errors
orthogonal to the regression line/plane/hyperplane.

   1. Is it possible to fit an error-in-variables (aka total least squares)
   model using MCMCglmm or would I have to use JAGS / STAN to do this?
   2. Is it possible to do this with multiple predictors in the same model
   (I have some models with 3 or 4 predictors, each measured with error)?