Package: jmBIG
Type: Package
Title: Joint Longitudinal and Survival Model for Big Data
Version: 0.1.3
Authors@R: c(person(("Atanu"), "Bhattacharjee",
                    email="atanustat@gmail.com",
	            role=c("aut", "cre","ctb")),
               person(("Bhrigu Kumar"), "Rajbongshi", role=c("aut","ctb")),
               person(("Gajendra K"), "Vishwakarma", role=c("aut","ctb")))
Maintainer: Atanu Bhattacharjee <atanustat@gmail.com>
Description: 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.  
Imports: JMbayes2,joineRML,rstanarm,FastJM,dplyr,nlme,survival,ggplot2
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
RoxygenNote: 7.3.1
NeedsCompilation: no
Repository: CRAN
Author: Atanu Bhattacharjee [aut, cre, ctb],
  Bhrigu Kumar Rajbongshi [aut, ctb],
  Gajendra K Vishwakarma [aut, ctb]
Packaged: 2025-01-19 20:41:40 UTC; atanubhattacharjee
Date/Publication: 2025-01-19 21:00:02 UTC
Built: R 4.6.0; ; 2026-04-25 14:02:52 UTC; unix
