Description: 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>.

Author: Paul D. McNicholas [aut, cre], K. Raju Jampani [aut] (May to Dec 2012), Sanjeena Subedi [aut]

Title: High Dimensional Longitudinal Data Analysis Using MCMC
role=c("aut", "cre","ctb")),person(("Akash"), "Pawar", role=c("aut","ctb")),person(("Bhrigu Kumar"),"Rajbongshi", role=c("aut","ctb")))
Description: High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC).
Currently support mixed effect regression with or without missing observations by considering
covariance structures. It provides estimates by missing at random and missing not at random assumptions.
In this R package, we present Bayesian approaches that statisticians and clinical
researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by

Title: Analysis of Multiple Time Course Data
Author: Rainer Opgen-Rhein and Korbinian Strimmer.
Description: This package contains general data structures and
functions for longitudinal data with multiple variables,
repeated measurements, and irregularly spaced time points.
It also implements a shrinkage estimator of dynamical correlation
Packaged: Fri Mar 10 15:28:39 2006; strimmer

Packaged: Tue Mar 28 15:15:23 2006; strimmer

Packaged: Tue Apr 25 16:44:01 2006; strimmer

Description: Contains general data structures and
Also implements a shrinkage estimator of dynamical correlation

Packaged: Mon Jun  5 17:16:56 2006; strimmer

Packaged: Wed Aug  9 14:05:19 2006; strimmer

Packaged: Sun Nov 16 23:59:58 2008; strimmer

repeated measurements, and irregularly spaced time points. It
also implements a shrinkage estimator of dynamical correlation
Packaged: Sat Oct 10 11:49:23 2009; strimmer

repeated measurements, and irregularly spaced time points.  It