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.

Time-Dependent Prognostic Accuracy with Multiply Evaluated Bio
Markers or Scores
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>.

Recursive partitioning of longitudinal data using mixed-effects models

Recursive partitioning of longitudinal data using mixed-effects
models

Recursive Partitioning of Longitudinal Data
Ross Jacobucci [aut,cre],
Sam Stewart[aut],
Mohamed Abdolell [aut],
Sarfaraz Serang [ctb],
Gabriela Stegmann [ctb]
R (>= 2.10), nlme, ggplot2, rpart, formula.tools, MASS
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>.

Model-Based Clustering and Classification for Longitudinal Data
P. D. McNicholas, K. Raju Jampani and Sanjeena Subedi
Clustering or classification of longitudinal data based on
a mixture of multivariate t or Gaussian distributions with a
Cholesky-decomposed covariance structure.

Orphaned on 2013-12-21 as maintainer address
<rjampani@uoguelph.ca> bounced.

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"))
Paul D. McNicholas [aut, cre],
K. Raju Jampani [aut] (May to Dec 2012),
Sanjeena Subedi [aut]
Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure.

Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure. Details in McNicholas and Murphy (2010) <doi:10.1002/cjs.10047> and McNicholas and Subedi (2012) <doi:10.1016/j.jspi.2011.11.026>.