mmCplusDpPost: Bayesian mixed effects model with a DP prior on by-subject...
In growcurves: Bayesian Semi and Nonparametric Growth Curve Models that Additionally Include Multiple Membership Random Effects

An internal function to dpgrowmm

1 2	mmCplusDpPost(y, X, Z, Wcase, Wsubject, Omega, omegaplus, groups, subjects, niter, nburn, nthin, strength.mm, shapealph, ratebeta)

`y`	An N x 1 response (of subject-measure cases)
`X`	Fixed effects design matrix
`Z`	Random effects design matrix. Assumed grouped by `subjects`
`Wcase`	An N x 1 multiple membership weight matrix to map supplemental random effects
`Wsubject`	An P.aff x S multiple membership weight matrix with rows equal to number of unique affected subjects
`Omega`	An S x S unnormalized adjacency matrix with entries equal to 1 where two effects communicate and 0, otherwise. Diagonal elements are zero
`omegaplus`	S x 1 vector of row sums of `Omega`
`groups`	S x 1 vector of group identifiers for each effect. Effects within each group communicate. Effects don't communicate across groups.
`subjects`	An N x 1 set of subject identifiers
`niter`	The number of MCMC iterations
`nburn`	The number of MCMC burn-in iterations to discard
`nthin`	The step increment of MCMC samples to return
`strength.mm`	The shape and rate parameters for the Γ prior on the CAR precision parameter, τ_{γ}
`shapealph`	The shape parameter for the Γ prior on the DP concentration parameter. The rate parameter is set of `1`.
`ratebeta`	The rate parameter for the Γ prior on the DP concentration parameter. Default value is `1`.