Employs a nonparametric formulation for bysubject 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.
Package details 


Author  Terrance Savitsky 
Maintainer  Terrance Savitsky <tds151@gmail.com> 
License  GPL (>= 2) 
Version  0.2.4.1 
Package repository  View on CRAN 
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