jmBIG: Joint Longitudinal and Survival Model for Big Data

Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences.

Version: 0.1.2
Depends: R (≥ 2.10)
Imports: JMbayes2, joineRML, rstanarm, FastJM, dplyr, nlme, survival, ggplot2
Published: 2024-03-20
DOI: 10.32614/CRAN.package.jmBIG
Author: Atanu Bhattacharjee [aut, cre, ctb], Bhrigu Kumar Rajbongshi [aut, ctb], Gajendra K Vishwakarma [aut, ctb]
Maintainer: Atanu Bhattacharjee <atanustat at>
License: GPL-3
NeedsCompilation: no
CRAN checks: jmBIG results


Reference manual: jmBIG.pdf


Package source: jmBIG_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): jmBIG_0.1.2.tgz, r-oldrel (arm64): jmBIG_0.1.2.tgz, r-release (x86_64): jmBIG_0.1.2.tgz, r-oldrel (x86_64): jmBIG_0.1.2.tgz
Old sources: jmBIG archive


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