Package: jmBIG
Type: Package
Title: Joint Longitudinal and Survival Model for Big Data
Version: 0.1.0
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
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
RoxygenNote: 7.2.3
NeedsCompilation: no
Repository: CRAN
Packaged: 2023-04-25 19:45:05 UTC; atanu
Author: Atanu Bhattacharjee [aut, cre, ctb],
  Bhrigu Kumar Rajbongshi [aut, ctb],
  Gajendra K Vishwakarma [aut, ctb]
Date/Publication: 2023-04-26 06:00:06 UTC
Built: R 4.1.2; ; 2023-04-27 11:51:16 UTC; unix
