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.

Documentation

Manual: growcurves.pdf
Vignette: None available.

Maintainer: Terrance Savitsky <tds151 at gmail.com>

Author(s): Terrance Savitsky

Install package and any missing dependencies by running this line in your R console:

install.packages("growcurves")

Depends R (>= 3.2.2), Rcpp(>=0.11.6)
Imports reshape2(>=1.2.1), Formula(>=1.0-0), ggplot2(>=1.0.1)
Suggests testthat(>=0.9.1)
Enhances
Linking to Rcpp(>=0.11.6), RcppArmadillo(>=0.5.000)
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Package growcurves
Materials
URL
Task Views Bayesian
Version 0.2.4.1
Published 2016-12-21
License GPL (>= 2)
BugReports
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks growcurves check results
Package source growcurves_0.2.4.1.tar.gz