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.
| Version: |
0.2.3.4 |
| Depends: |
Rcpp (≥ 0.10.1), RcppArmadillo (≥ 0.3.4.4), reshape2 (≥
1.2.1), scales (≥ 0.2.0), ggplot2 (≥ 0.9.2), Formula (≥
1.0-0), testthat (≥ 0.5) |
| LinkingTo: |
Rcpp, RcppArmadillo, reshape2, scales, ggplot2, Formula, testthat |
| Published: |
2013-03-16 |
| Author: |
Terrance Savitsky |
| Maintainer: |
"terrance savitsky" <tds151 at gmail.com> |
| License: |
GPL (≥ 2) |
| NeedsCompilation: |
yes |
| In views: |
Bayesian |
| CRAN checks: |
growcurves results |