The data that is generated from consecutive 'GillespieSSA' runs for a generic biochemical network
    is formatted as "rows". The first column of each row constitutes the computed timestep. Subsequent 
    columns are used for the participating molecules of a generic biochemical network. In this way 'TemporalGSSA', 
    may be considered a wrapper for the R-package 'GillespieSSA'. The number of observations must be at least 30. 
    This will generate data that is statistically significant. The user must also enter an integer from 1-4.
    These specify the statistical modality utilized to compute a representative timestep (mean, median, random, all).
    These arguments are mandatory and will be checked. Whilst, the numeric indicator "0" indicates suitability,
    "1" prompts the user to revise and re-enter their data. An optional logical argument controls the output to the 
    console with the default being "TRUE" (curtailed) whilst "FALSE" (verbose). The temporal profile of a molecule
    is necessary to comprehend its' behaviour within the cell. This is accomplished by selecting a representative 
    timestep for a set of observations or consecutive runs (n >= 30). A linear model of the numbers of each molecule is 
    created with the associated timestep from these observations. The coefficients of this model (slope, constant) are then
    incorporated into a second linear regression model. Here, the independent variable is the representative timestep 
    chosen previously. The generated data is the imputed molecule number for an in silico experiment with (n >=30) 
    observations. These steps can be replicated with multiple set of observations or runs. The generated "technical 
    replicates" can be averaged and will constitute the time-dependent data point of each molecule for a particular simulation
    time. For varying simulation times these data will generate time-dependent trajectories for each molecule
    of the biochemical network under study. The algorithm has been deployed effectively in previous publications Kundu, S 
    (2021, Heliyon) <doi:10.1016/j.heliyon.2021.e07466> and (2016, Journal of Theoretical Biology) <doi:10.1016/j.jtbi.2016.07.002>.