Title: High Dimensional Longitudinal Data Analysis Using MCMC
Package: longit
Version: 0.1.0
Date: 2021-04-06
Depends: R (>= 2.10)
Imports: AICcmodavg, missForest,R2jags,rjags,utils
LazyData: Yes
LazyDataCompression: xz
ByteCompile: Yes
Authors@R: 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")))
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 
             Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Maintainer: Atanu Bhattacharjee <atanustat@gmail.com>
RoxygenNote: 7.1.1.9000
Packaged: 2021-04-13 17:02:31 UTC; atanu
Author: Atanu Bhattacharjee [aut, cre, ctb],
  Akash Pawar [aut, ctb],
  Bhrigu Kumar Rajbongshi [aut, ctb]
Repository: CRAN
Date/Publication: 2021-04-15 08:00:05 UTC
Built: R 4.6.0; ; 2026-04-26 08:30:02 UTC; unix
