This is a collection of tools to allow the medical professional to
 calculate appropriate reference ranges (intervals) with confidence intervals around
 the limits for diagnostic purposes.

A suite of methods to fit and predict case count data using 
    a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible) 
    model, based on an assumed specification of the effective reproduction 
    number. The significance of this approach is that it relates epidemic 
    progression to the average number of contacts of infected individuals, 
    which decays as a function of the total susceptible fraction remaining 
    in the population. The main functions are pred.curve(), which computes 
    the epidemic curve for a set of parameters, and estimate.mle(), which 
    finds the best fitting curve to observed data. The easiest way to pass 
    arguments to the functions is via a config file, which contains input 
    settings required for prediction, and the package offers two methods, 
    navigate_to_config()  which points the user to the configuration file, 
    and re_predict() for starting the fit-predict process.
    Razvan G. Romanescu et al. (2023) <doi:10.1016/j.epidem.2023.100708>.

Indirect method for the estimation of reference intervals using 
	Real-World Data ('RWD'). It takes routine measurements of diagnostic 
	tests, containing pathological and non-pathological samples as input 
	and uses sophisticated statistical methods to derive a model describing 
	the distribution of the non-pathological samples. This distribution can 
	then be used to derive reference intervals. Furthermore, the package 
	offers functions for printing and plotting the results of the algorithm. 
	See ?refineR for a more comprehensive description of the features. 
	Version 1.0 of the algorithm is described in detail in 'Ammer et al. (2021)' 
	<doi:10.1038/s41598-021-95301-2>. Additional guidance on the usage of 
	the algorithm is given in 'Ammer et al. (2023)' <doi:10.1093/jalm/jfac101>.