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: 0.1.3 Depends: R (≥ 2.10) Imports: JMbayes2, joineRML, rstanarm, FastJM, dplyr, nlme, survival, ggplot2 Published: 2025-01-19 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 gmail.com> License: GPL-3 NeedsCompilation: no CRAN checks: jmBIG results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=jmBIG to link to this page.
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