Author: Stephen Tueller. Funded by the National Institute on Drug Abuse (NIDA) Award number 1R03DA030850, the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Award Number R03 AA019775, and the National Institute of Justice Award Number 2011-RY-BX-0003.
Suggests: RColorBrewer, colorspace, MASS, ggplot2

Title: Time-Dependent Prognostic Accuracy with Multiply Evaluated Bio
Description: Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.

Title: Recursive partitioning of longitudinal data using mixed-effects models
Author: Sam Stewart and Mohamed Abdolell

Title: Recursive partitioning of longitudinal data using mixed-effects

Title: Recursive Partitioning of Longitudinal Data
Depends: R (>= 2.10), nlme, ggplot2, rpart, formula.tools, MASS
Description: Performs recursive partitioning of linear and nonlinear mixed effects models,
specifically for longitudinal data. The package is an
extension of the original 'longRPart' package by
Stewart and Abdolell (2013) <https://cran.r-project.org/package=longRPart>.

Title: Model-Based Clustering and Classification for Longitudinal Data
Author: P. D. McNicholas, K. Raju Jampani and Sanjeena Subedi
Maintainer: K. Raju Jampani <rjampani@uoguelph.ca>
Description: Clustering or classification of longitudinal data based on
a mixture of multivariate t or Gaussian distributions with a

X-CRAN-Original-Maintainer: K. Raju Jampani <rjampani@uoguelph.ca>

Authors@R: c(person("Paul D.", "McNicholas", role = c("aut", "cre"), email = "mcnicholas@math.mcmaster.ca"), person("K. Raju", "Jampani", role = "aut", comment = "May to Dec 2012"), person("Sanjeena", "Subedi", role = "aut"))
K. Raju Jampani [aut] (May to Dec 2012),
Maintainer: Paul D. McNicholas <mcnicholas@math.mcmaster.ca>
Description: Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure.