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Bayesian Semi and Nonparametric Growth Curve Models that Additionally Include Multiple Membership Random Effects.

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.

Maintainer: "Savitsky, Terrance"
Author(s): Terrance Savitsky

License: GPL (>= 2)

Uses: Formula, ggplot2, Rcpp, reshape2, testthat

Released about 3 years ago.

14 previous versions



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