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

High Dimensional Longitudinal Data Analysis Using MCMC
c(person(("Atanu"), "Bhattacharjee",
email="atanustat@gmail.com",
role=c("aut", "cre","ctb")),person(("Akash"), "Pawar", role=c("aut","ctb")),person(("Bhrigu Kumar"),"Rajbongshi", role=c("aut","ctb")))
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
Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.
Atanu Bhattacharjee [aut, cre, ctb],
Akash Pawar [aut, ctb],
Bhrigu Kumar Rajbongshi [aut, ctb]

Rainer Opgen-Rhein and Korbinian Strimmer.
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
and dynamical covariance.

Contains general data structures and
functions for longitudinal data with multiple variables,
repeated measurements, and irregularly spaced time points.
Also implements a shrinkage estimator of dynamical correlation
and dynamical covariance.

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
and dynamical covariance.

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
and dynamical covariance.