A mainly instrumental package meant to allow other packages whose core is written in 'C++' to read, write
        and manipulate matrices in a binary format so that the memory used for them is no more than strictly needed. Its functionality
        is already inside 'parallelpam' and 'scellpam', so if you have installed any of these, you do not need to install 'jmatrix'.
        Using just the needed memory is not always true with 'R' matrices or vectors, since by default they are of double type. Trials
        like the 'float' package have been done, but to use them you have to coerce a matrix already loaded in 'R' memory to a float matrix,
        and then you can delete it. The problem comes when your computer has not memory enough to hold the matrix in the first place, so
        you are forced to load it by chunks. This is the problem this package tries to address (with partial success, but this is a
        difficult problem since 'R' is not a strictly typed language, which is anyway quite hard to get in an interpreted language).
	This package allows the creation and manipulation of full, sparse and symmetric matrices of any standard data type.

Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.

Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).

Provides model fitting, prediction, and plotting for joint models of longitudinal and multiple time-to-event data, including methods from Rizopoulos (2012) <doi:10.1201/b12208>. Useful for handling complex survival and longitudinal data in clinical research.

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.