High Dimensional Longitudinal Data Analysis Using MCMC
Overview
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.
Install
Health
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespace in Imports field not imported from: ‘utils’ All declared Imports should be used.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 14
- Examples that run
- 100%10 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 71%
Downloads
Dependencies
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.1.0Latest2021-04-15 · current release
- RR 4.0.0 released · 2020-04-24
Package metadata
- First published
- 2021-04-15
- Total releases
- 1 / 5 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 2.8 KB / 4 files
- Download size
- 13 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("longit")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.