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: 2026-06-08 06:49:45 UTC; root
Author: Atanu Bhattacharjee [aut, cre, ctb], Akash Pawar [aut, ctb],
        Bhrigu Kumar Rajbongshi [aut, ctb]
Config/pak/sysreqs: make jags libicu-dev
Repository: https://atanubhattacharjee.r-universe.dev
Date/Publication: 2021-04-15 07:00:05 UTC
RemoteUrl: https://github.com/cran/longit
RemoteRef: HEAD
RemoteSha: 2aa442df666057b016cc191b5da3e182dda3eacc