Joint Longitudinal and Survival Model for Big Data R package jmBIG version 0.1.3 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. Software Depends: R (>= 2.10) Imports: JMbayes2,joineRML,rstanarm,FastJM,dplyr,nlme,survival,ggplot2 Atanu Bhattacharjee <atanustat@gmail.com> Comprehensive R Archive Network (CRAN) Atanu Bhattacharjee [aut, cre, ctb], Bhrigu Kumar Rajbongshi [aut, ctb], Gajendra K Vishwakarma [aut, ctb] GPL-3 2025-01-19 application/tgz https://CRAN.R-project.org/package=jmBIG doi:10.32614/CRAN.package.jmBIG