longit: High Dimensional Longitudinal Data Analysis Using MCMC

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>.

Version:	0.1.0
Depends:	R (≥ 2.10)
Imports:	AICcmodavg, missForest, R2jags, rjags, utils
Published:	2021-04-15
DOI:	10.32614/CRAN.package.longit
Author:	Atanu Bhattacharjee [aut, cre, ctb], Akash Pawar [aut, ctb], Bhrigu Kumar Rajbongshi [aut, ctb]
Maintainer:	Atanu Bhattacharjee <atanustat at gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	longit results

Documentation:

Reference manual:

longit.pdf

Downloads:

Package source:	longit_0.1.0.tar.gz
Windows binaries:	r-devel: longit_0.1.0.zip, r-release: longit_0.1.0.zip, r-oldrel: longit_0.1.0.zip
macOS binaries:	r-release (arm64): longit_0.1.0.tgz, r-oldrel (arm64): longit_0.1.0.tgz, r-release (x86_64): longit_0.1.0.tgz, r-oldrel (x86_64): longit_0.1.0.tgz

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