Title: Bayesian semiparametric growth curve models that additionally
include multiple membership random effects.
Description: Employs a Dirichlet Process (DP) prior on the set of
by-subject random effect parameters under repeated waves of
measurements to allow the number of random effect parameters
specified per subject, \code{q}, to be equal to the number of
measurement waves, \code{T}.  Random effects are grouped by
subject and all \code{q} parameters receive the DP prior.  An
additional set of random effects are included that utilize a
different grouping factor; e.g. treatment(s) exposure or
dosage.  These additional random effects are mapped back to
subjects through a multiple membership weight matrix.
Depends: Rcpp (>= 0.9.4), RcppArmadillo (>= 0.2.29), reshape2 (>=
LinkingTo: Rcpp, RcppArmadillo, reshape2, scales, ggplot2, Formula,
Collate: 'summary_quantiles.R' 'mcmcPlots.R' 'relabel.R' 'getmf.R'
'XZcov.R' 'growthCurve.R' 'dpgrowmm.R' 'dpgrow.R' 'help.R'

Title: Bayesian semi and nonparametric growth curve models that
additionally include multiple membership random effects.
Description: Employs a non-parametric formulation for by-subject random
effect parameters to borrow strength over a constrained number
of repeated measurement waves in a fashion that permits
multiple effects per subject.  One class of models employs a
Dirichlet process (DP) prior for the subject random effects and
includes an additional set of random effects that utilize a
different grouping factor and are mapped back to clients
through a multiple membership weight matrix; e.g. treatment(s)
exposure or dosage.  A second class of models employs a
dependent DP (DDP) prior for the subject random effects that
directly incorporates the multiple membership pattern.
Depends: Rcpp (>= 0.9.13), RcppArmadillo (>= 0.3.2), reshape2 (>=

Depends: Rcpp (>= 0.10.1), RcppArmadillo (>= 0.3.4.4), reshape2 (>=

Description: Employs a non-parametric formulation for by-subject random effect
parameters to borrow strength over a constrained number of repeated
measurement waves in a fashion that permits multiple effects per subject.
One class of models employs a Dirichlet process (DP) prior for the subject
random effects and includes an additional set of random effects that
utilize a different grouping factor and are mapped back to clients through
a multiple membership weight matrix; e.g. treatment(s) exposure or dosage.
A second class of models employs a dependent DP (DDP) prior for the subject
random effects that directly incorporates the multiple membership pattern.
LinkingTo: Rcpp (>= 0.10.6), RcppArmadillo (>= 0.4.000)
Imports: RcppArmadillo (>= 0.4.000), reshape2 (>= 1.2.1), scales (>=
Collate: 'getmf.R' 'XZcov.R' 'datbrghtmodterms.R' 'datbrghtterms.R'